分光反射率データベースの構築と皮膚組織における光吸収の評価への応用
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言語: English
出版者:
公開日: 2021-06-23
キーワード (Ja):
キーワード (En):作成者: カウスタフ, ダス
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URL

https://doi.org/10.15118/00010395

Construction of Spectral Reflectance Database andits Application to the Evaluation of Light
Absorption in Skin Tissue

Dissertation Submitted for the degree ofDoctor of Engineering
December 2020

byKaustav Das

Division of Engineering, Doctor’s Course
Muroran Institute of Technology
Muroran, Hokkaido, Japan

CONTENTS
Chapter 1. Introductory Remarks

1

1.1 General Review for Tissue Optics

1

1.2 Motivation

8

1.3 Synopsis of Contents

9

Chapter 2. Fundamental Theories and Principles

11

2.1 Physiology and Anatomy of Human Skin

112.1.1 Introduction

11

2.1.2 Cells Elements

11

2.1.3 Fibers Elements

13

2.1.4 Chromophores

15

2.2 Constitution of Skin Tissue

16

2.2.1 Introduction

16

2.2.2 Epidermis

18

2.2.3 Dermis

29

2.2.4 Subcutaneous Tissue

21

2.2.5 Skin Layers

21

2.2.6 Hairs

22

2.2.7 Skin Surface Lipid

22

2.3 Optical Propagation Theory in Biological Tissue23

2.3.1 Spectral Reflectance and Transmittance23

2.3.2 Lambert-Beer’s Law

24

2.3.3 Spectrocolorimetry

25

2.3.4 Photon Fluence30

2.3.5 Point Spread Function of Reflected Intensity

31

2.4 Multiple Regression Analysis

32

2.5 Monte Carlo Modeling of Light Transport in Scattering Medium

33

2.5.1 Optical Properties

33

2.5.2 Scattering Phase Function

38

2.5.3 Monte Carlo Method

39ii

2.5.4 Inverse Monte Carlo Method50

2.6 Optical Properties of Skin Tissue Components

50

2.6.1 Scattering by Tissue50

2.6.2 Absorption by Biological Chromophores

51

2.6.3 Refractive Index of Tissue

57

2.7 Layered Skin Tissue Model

57

2.8 Spectral Reflectance Database61

2.9 Agarose-gel Phantom

62

Chapter 3. Effects of Detection Area on Measurementsof Spectral Reflectance in Human Skin Tissue64

3.1 Introduction

64

3.2 Model of Light Propagation in Skin Tissue

65

3.2.1 Skin Tissue Model

65

3.2.2 Monte Carlo Simulation

66

3.3 Numerical Investigation

69

3.3.1 Method and Condition69

3.3.2 Point-like Illumination

70

3.3.3 Circular-spot Illumination

74

3.4 Experimental Investigation

80

3.4.1 Method and Apparatus

80

3.4.2 Results

81

3.5 Conclusion

83

Chapter 4. Simulated Reflectance Spectra and Point Spread
Functions in Database Constructed by ModerateGrouping of Nine Layers in Skin Model84

4.1 Introduction

84

4.2 Simulation Model for Spectral Reflectance87

4.2.1 Skin Tissue Model

87

4.2.2 Monte Carlo Simulation

88iii

4.3 Simulated Reflectance Spectra and Point Spread Function
in a Standard Condition

90

4.4 Construction of Database

92

4.4.1 Spectral Fitting

92

4.4.2 Spectral Database

92

4.4.3 Numerical Validation Experiment97

4.4.4 Ambiguity Problem and Identification with Point Spread Function

102

4.5 Conclusion

112

Chapter 5. Spectral Classification and Mining in
Simulated Reflectance Database for Detectionof Absorption Change in Skin Tissue

114

5.1 Introduction

114

5.2 Database and Spectral Mining

116

5.2.1 Nine-Layered Skin Tissue Model and Monte Carlo Simulation

116

5.2.2 Construction of Database by Moderate Grouping

118

5.2.3 Spectral Classification and Mining

121

5.3 Phantom Experiments

125

5.3.1 Agarose-gel Phantom

125

5.3.2 Five types of Spectral Change in Phantoms

129

5.3.3 Blind Tests for Phantom

133

5.4 Pressure-Cuff Occlusion Experiment136

5.5 Hot Water Immersion Experiment141

5.6 Conclusion

147

Chapter 7. Concluding Remarks

149

Appendix

153

A.1

153

A.2

154

A.3

155

iv

A.4

156

A.5

157

A.6

158

Acknowledgement

160

Bibliography

161

List of Papers by the Author

180v

Chapter 1
Introductory Remarks

1.1

General Review for Tissue Optics
In recent years, the use of light for clinical diagnostics and therapeutic medicine

has been attracted as the non-invasive technique and, thus, various optical methods havebeen studied. The visible to near-infrared light propagated through the biological tissue
is strongly influenced by many chromophores within the tissue. Human skin is a typicalexample of such biological object. There are many chromophores in the skin tissue suchas melanin, carotene, bilirubin, hemoglobin, and so on. These chromophores are related
to physiological conditions of the tissue and, have their own optical absorption properties.
For example, the color of venous and arterial blood is different in an oxygen state. The
pigmentation or tanning of skin is caused by the multiplication of melanin due to thephysiological response.

Therefore, if the optical properties of the tissue can be

successfully measured, the useful information on health states and metabolisms of living
tissue can be obtained through the skin. Alternatively, the optical properties may berequired to calculate the distribution of light in the tissue during photodynamic therapy
or laser surgery. Thus, the optical techniques have a large potential in the biomedical field.
In clinical situation, however, there are only a few reliable instruments based on opticalmethods. This is because the biological tissues are strongly scattering media and, thus,
the determination of optical properties is generally difficult by the conventional opticalmethods such as the ray optics and attenuation according to the Lambert-Beer law. For
this reason, various theories and methods have been studied to model the light1

propagation in the tissue-like scattering media.
The quantitative distribution of light intensity in scattering media can be describedfrom the solution of Chandrasekhar’s radiative transfer equation [1] as,  sdI (r, s) ( a  s ) I (r, s)  ap(s, s) I (r, s) dΩds4 4

(1.1)

where I (r, s) is the intensity per unit solid angle at target location r in the direction s (s isthe directional unit vector),  a and  s are the absorption coefficient and scattering
coefficient of medium, respectively. p(s, s) is the scattering phase function, representing
scattering contribution from the direction s to s , and dΩ is the solid angle. The firstterm on the right-hand side of Eq. (1.1) represents the loss in I (r, s) per unit length indirection s due to absorption and scattering. The second term describes the gain in

I (r, s) per unit length in direction s due to scattering from other scattered light I (r, s)dΩ (i.e. light intensity confined in the elemental solid angle dΩ ) from direction s .
This integro-differential equation expresses the rate of change in the intensity ofa narrow incident light beam as a function of the optical properties of the medium
involved. Although the radiative transfer theory gives a more adequate description of the
distribution of light intensity in the optically turbid medium than does any other model,
the general analytic solution is not known yet. However, there are some approximate
analytical theories or numerical method in the following.
Diffusion Approximation. The diffusion approximation is obtained by considering
the lowest-order approximation of the expansion in spherical harmonics of the radiativetransfer equation. The diffusion approximation can be solved for homogeneous diffusing
media for different boundary conditions such as infinitely extended medium, semi-infinite
slab, finite slab, cylinder, and sphere. Theoretical studies of light propagation in the2multiple scattering media based on the diffusion approximation have been published by
Ishimaru [2] and Furustu [3]. The solutions obtained well describe photon propagation
when the distance of a detector from the source is larger than about 10 transport mean
free paths.
Random Walk Method. The motion of a photon in the scattering medium isapproximated as a random walk on a simple cubic lattice. The photon proceeds throughthe lattice as steps to one of six nearest neighboring lattice points. By using this simple
scheme, it is possible to obtain analytical relationships similar to the ones obtained using
the diffusion approximation [4].
Kubelka-Munk Theory. The Kubelka-Munk theory has been widely used todetermine the absorption and scattering coefficients of turbid media.

This theory

considers the two opposite fluxes, and can be employed when the scattering issignificantly dominant over the absorption.

This model is not accurate for forward-

directed scattering and complicated geometric media such as a biological tissue because
the isotropic scattering is assumed in this theory. Recently, however, 3-, 4-, and 7-flux
models are also studied as the special cases of the multi-flux theory [5-9]. Applying to
the photon-tissue interactions, 3-flux model includes two diffuse fluxes (forward andbackward) and collimated forward flux, and 7-flux model consists of six diffuse fluxes in
3D space and collimated forward flux.
Monte Carlo Method. The Monte Carlo method provides a physical simulationof photon propagation in the scattering medium. The model assumes a non-deterministic,
stochastic nature for high scattering and absorption of individual photon [10,11]. By
using a random number generating routine, the trajectories of emitted photons are chosen
according to the statistical rules relevant for photon propagation through the medium.

3

The trajectory of any emitted photon is followed until it exits from the scattering medium
or it arrives at the detector.

The Monte Carlo method allows a full-dimensionaldescription of photon propagation and the actual scattering properties of tissue can betaken into account in the simulation.

The parameters of a scattering coefficient,absorption coefficient, and scattering phase function are usually required in this method.
This Monte Carlo method requires the long computational time for the reliable results.
Various techniques based on the light propagation model mentioned above havebeen studied and reported for measuring the optical properties of tissue. They are roughlyclassified into the three types of measuring technique, the time-resolved measuring
technique, the frequency-domain measuring technique, and the spatially resolved
measuring technique. An outline of these types is briefly described in the following.

(i)Time-Resolved Measureing Technique
When an ultrashort light pulse (a few picoseconds long) is incident on the

scattering medium such as tissues, the received pulse backscattered from, or transmitted
thorough the medium is a few seconds long. This temporal spreading of a light pulse iscalled the temporal spreading function (TPSF) [12], and depends on the optical propertiesof the medium. Therefore, the TPSF can be used to acquire the absorption and scattering
coefficients of tissues. The use of a source emitting ultrashort pulse (picosecond,
femtosecond) and an ultrafast detector such as a streak camera makes possible a direct
measurement of the TPSF of the received pulse, which is known as the time-resolved
measurement. Chance et al. [13] have reported the usefulness of the time-resolved
technique for measuring the oxygenation state in the brain. Delpy et al. [14], Wilson etal. [15], and Nomura et al. [16] also utilized the time-resolved technique for measuring

4the optical properties of tissue. Patterson et al. [17] investigated the behavior of light intissue by using the time-dependent diffusion approximation, whereas Hasegawa et al. [18]and Delpy et al. [14] employed the Monte Carlo method to analyze the time-resolved
transmittance. Jacques [19] described light irradiance at various time intervals after
impingement of femtosecond and picosecond light pulses using the Monte Carlo method.
In recent years, some researchers have reported on the possibility of the opticalcomputed tomography (CT) based on the time-resolved techniques numerically andexperimentally. Ito et al. [20] have obtained an image of oxygenation change in a rat
brain by using the optical-CT scanner based on the conventional X-ray CT algorithm.
Araki and Nashimoto [21] developed a deconvolution technique using the point spreadfunctions and succeeded in reconstruction of an optical-CT image experimentally. Using
the time dependent diffusion equation, Arridge et al. [22] analytically and experimentallystudied a reconstruction method as an inverse method. Singer et al. [23] proposed
numerically a new algorithm for using the six-flux model for radiation and an iterative
method for reconstruction. For the instrumentation of optical-CT, Yamada et al. [24, 25]
have investigated the use of temporal variations of transmittance of a light impulse
numerically.

(ii)

Frequency-Domain Measuring Technique
Frequency-domain photon migration [FDPM] methods, in which the light source

intensity is modulated at high frequency, have been successfully applied to in vitro
spectroscopy studies of turbid media [26-30]. Analytical solutions to the frequencydomain standard diffusion equation (SDE) have been employed in these studies to deduce
the absorption and scattering properties from the frequency-domain data acquired in the5optical turbid media. The frequency-domain SDE has been evaluated to be applicable tothe study of thick, turbid, and macroscopically homogenous scattering media when thesource-detector separation is typically greater than 1 cm and the source modulation
frequency is less than 1 GHz [31]. Fishkin et al. [32] measured the optical properties ofnormal and malignant in vivo tissue by using the FDPM method.

(iii)

Spatially Resolved Measuring Technique
When a narrow photon beam is incident on the homogeneous scattering medium,the radial dependence of backscattered light depends on the optical properties of themedium. The spatially resolved technique is based on this radial dependence of the
diffuse reflectance from the scattering medium. Different physical models have beenused to determine expressions for the dependence of the diffuse reflectance on radial
distance from the incident point of light. Groenhuis et al. [33,34] utilized the diffusion
theory to derive the Green’s function for photon fluence resulting from an isotropic point
source at depth in a semi-infinite tissue as an infinite sum of modified Bessel functions.
Shmitt et al. [35] extended this approach to express the radial reflectance from a multilayered tissue such as the skin. Wilson et al. [36] proposed the use of the relativereflectance curve to determine the tissue optical properties. Farrell et al. [37] conducted
the spatially resolved measurement of diffuse reflectance by using the multiple fiber-optic
detectors in contact with the tissue surface at varying distances from the source. To avoid
the pressure due to the contact probe, Bolt and ten Bosch [38,39] introduced an alternativetechnique based on a remote, non-contact, video camera detector (video reflectometry).
Jacques et al. [40] used this technique to measure the ex vivo tissue. Results obtained
from the similar instrumentations have also been reported by Splinter et al. [41], Dogariu

6and Asakura [42], and Kienle et al. [43]. More recently, Wang and Jacques [44] proposed
the use of a laser beam with an oblique incidence to deduce the reduced scattering
coefficient of a turbid medium. Lin et al. [45] extended this technique to measure theabsorption coefficient and reduced scattering coefficient simultaneously.
Spectrophotometry over a broad range from visible to near-infrared wavelength
has also become a common method to measure the reflectance spectra of tissue. Edwards
et al. [46], Kuppenhiem et al. [47], Anderson and Parrish [48], and Feather et al. [49],employed the reflectance spectra to investigate the relation between the color of humanskin and chromophores. Spectral feature analysis has been studied to evaluate the various
physiological conditions of living tissue.

Diffey et al. [50] have studied thevasoconstriction and erythema by using the numerical indices derived from thereflectance spectra of human skin.

Dawson et al. [51] utilized the reflectancespectrophotometry for measurement of human skin tissue to characterize the spectral
properties. Harrison et al. [52] also measured the oxygen saturation and concentration ofhemoglobin in the human skin tissue during the tuberculin reaction by using the numericalindices based on the reflectance spectra. Marchesini et al. [53,54] and Wallace et al. [55]have studied the method to discriminate between benign and malignant lesion in the skintissue by using features of reflectance spectra of human skin.
As the other spectral analysis, chemometrics have been studied for evaluating thechromophores in the skin tissue. Tsumura et al. [56] proposed a method using theindependent component analysis of color images for visualizing spatial distributions ofmelanin and hemoglobin in the human skin. Shimada et al. [57-59] reported a method
for predicting absorption spectra of skin tissue using the multiple regression analysis
based on the modified Lambert-Beer law [ 60-63].

7

1.2

Motivation
Analysis of spectral reflectance of skin has been widely preferred as an integral

part to obtain necessary knowledge of skin conditions and to some extent physiologicalconditions. As change in physiological and skin conditions [64-67] are believed to be
responsible for alteration of chromophores concentration (mainly melanin, andoxygenated and deoxygenated hemoglobin) and tissue micro-structure, it leads tovariation in spectral reflectance. Thus, it becomes essential to find some relation between
spectral shape, chromophores and tissue morphology. Also, it is well known that melanincorresponds to absorption coefficient in epidermal layer and hemoglobin to absorption
coefficients of dermal and subcutaneous layers. Although, such relations are difficult toanalyze in general.
In regard to this, many researchers have utilized various methods to evaluate theconnection between skin condition and spectral reflectance. For example, diffusion
approximation [68,69], solution of radiative transfer equation using auxiliary function [70,71], and most widely used statistical method including principal component analysis [72],independent component [56] analysis and multiple regression analysis [57-59, 73-76]. Allof these method have the common drawback of fitting respective mathematical models todifferent skin conditions including healthy or normal to diseased or abnormal.
By referring to the idea of Zang et. al. [69] to use library-based spectral fitting for
identification of optical property from measured spectra, in this dissertation a method has
been proposed using spectral mining from database consisting of simulated spectra usinga nine-layered skin tissue model. The method is used to evaluate mainly the change ofabsorption conditions in measured spectra, as change in absorption condition corresponds
to change in chromophores concentration.

8

1.3

Synopsis of Contents
This dissertation is aimed at the investigation on evaluation of changing

absorption property of measured spectra using spectral mining from database consistingof simulated spectral reflectance curves. This study can mainly be segregated into twoparts. In the first part, construction of the spectral database is discussed including thenumerical investigation on the database, In the second part, actual spectral measurements
were carried out under some specific conditions in order to analyze mainly absorption
property of measured spectra using spectral mining from the database. The fundamental
theories and principles that are integral to this dissertation are introduced in the chapter 2.
Chapter 3 highlights the effect of a detecting aperture size on spectral reflectancemeasurement of human skin through numerical simulation and experiments. To do so,Monte Carlo simulation of light propagation in the nine-layered skin tissue model was
employed to generate simulated spectral reflectance curves and photon fluence map, andspectrophotometric experiments was carried out using an optical setup with an integrating
sphere. This particular study becomes functional for the study made in chapter 5, where
spectra are measured from human subject in order to show the effectiveness of the spectral
mining method.
Chapters 4 and 5 are assigned to the proposed method of spectral mining from thespectral reflectance database to analyze unknown optical parameters of measured spectra.
In chapter 4, the construction of the database which consists of simulated spectra withvarying absorption and scattering coefficients is discussed. To make the construction of
database in a convenient way, absorption coefficients are made into two groups andscattering coefficients into three groups. This resulted in five types of spectral change insimulated spectra which are included in the database. Among the five types, two types

9

presented similar spectral change with different absorption and scattering coefficients,
which leads to an ambiguity problem in the constructed database. The possibility of these
five types is investigated numerically for spectral change in evaluating unknown opticalparameters of measured spectra.
In chapter 5, the five types of spectral change in simulated spectra are validated
by observing five types of spectral change in measured spectra from three-layered
agarose-gel phantom. Pressure-cuff occlusion and hot water immersion experiments arecarried out on human forearm to confirm the applicability to analysis of change inabsorption coefficients of measured spectra as both the experiments are supposed to vary
hemoglobin concentration or absorption coefficients in dermal and subcutaneous layers.
Also, use of application based customized database is briefly described as a possible way
to overcome the ambiguity of the original full-size database.
Chapter 6 provides an overall summary of the various results obtained by studies
executed in this dissertation.

10Chapter 2.
Fundamental Theories and Principles

2.1 Physiology and Anatomy of Human Skin

2.1.1 Introduction
The optical and visual properties of skin components at each level of ourtaxonomy differ significantly depending on their anatomical characteristics. For example,
light scattering behaviors of cells and fibers depend on their sizes and shapes. Light
propagation in the skin layers, i.e. the epidermis and the dermis, is very different since
their structures, densities and thicknesses vary greatly. Reflection at the surface of skin is
influenced by the morphological characteristics of fine wrinkles. The appearances of
wrinkles themselves also depend on their morphological characteristics such as depth,
width and density variations. Most of these optical and visual properties are different fordifferent body regions and body parts since the anatomical characteristics of the lowerlevel components vary across the body. The above examples are used to convey theimportance of understanding the anatomical properties of each of the skin components.

2.1.2 Cells Elements
Although skin is composed of various types of cellular level elements, cells, fibers
and chromophores are of special relevance to us. This is because light scattering andabsorption in these fundamental elements are the building blocks of the gross opticalphenomena observed at the cellular level. Skin includes various types of cells. The main11

cells are keratinocyte, fibroblast, fat cell, melanocyte and red blood cells. These cells arepresent in different locations and have different structures and functions. Keratinocytes
are quantitatively the dominant constituent cells in the epidermis. These cells produce
fibriform proteins called keratin which contribute to the rigidity of the outermost layer ofskin.
Keratinocytes protect the body from the external environment, for instance fromstimulation, friction and viruses, while retaining moisture. Keratinocytes can be further
categorized into four types of cells based on their functions and structures: basal cells,
prickle cells, granular cells and horny cells. Although these cells have the same origin,
they have different shapes, functions and sub-cellular level elements called organelles.
For example, the basal cell, which reproduces keratinocytes, is a cylindrical and softliving cell. On the other hand, the horny cell, which mainly acts as a protector from theexternal environment, is a very flat and hard dead cell in which most organelles aredegenerate.
Fibroblasts are long and narrow cells present in the dermis, the second skin layer
beneath the epidermis. They produce collagen and elastin fibers which are the primaryconstituents of the dermis.
Fat cells are quantitatively the most abundant cells of the dermis. These cells
accumulate fat and their sizes vary according to the volume of fat contained in them.
These cells do not absorb much light. On the other hand, melanocyte and erythrocyte cells,
both of which contain chromophores, mainly absorb light.
Melanocytes carry melanin which is one of the main light-absorbing pigments inskin. There are generally 1,000 to 2,000 melanocytes in 1 mm2 of skin. This cell contains
specialized organelles called melanosomes. When skin is exposed to sunlight,

12melanosomes are activated and produce melanin. The density of melanosomes depends
on the body region. For example, regions that are frequently exposed to sunlight, such as
the face, have higher density than other regions.
Red blood cells are the carriers of hemoglobin, another light-absorbing pigmentin skin. Red blood cells have biconcave structures. The diameter of an erythrocyte isapproximately 5 μm. Red blood cells usually contain more than 300 mg/ml of hemoglobin
and carry oxygen from the lungs to tissues and carbon dioxide from tissues to the lungs.
A typical cell is composed of a cell membrane and organelles such as nucleus,
mitochondria, lysosome, cytoplasm, Gelid apparatus, endoplasmic reticulum, etc. The
nucleus, which is the largest spherical organelle, ranges from 3 to 10 μm in size and is
enclosed in a membrane called the nuclear envelope. The nucleus includes most of the
DNA of a cell and serves as the storage area for genetic information. The mitochondrion
is 0.5 to 1.5 μm in size and is an oval-shaped organelle composed of a double membrane.
The mitochondrion generates energy from food. The cell membrane is the outermost layerof a cell and has a doubly-layered structure of lipids (bilayer membrane). The thicknessof the cell membrane is approximately 15 nm.

2.1.3 Fibers Elements
Skin contains several types of fibers. Keratin, collagen and elastin will beconsidered here as typical types of fibers found in skin.
Keratin fibers are mainly found in the outer-level epidermal cells, including horny
cells. These fibers protect the inner side of skin from the external environment. At thesame time, they contribute to moisture-retention in skin by holding water. The length anddiameter of these fibers depend on the amount of moisture they actually hold.

13

Collagen fibers are the main constituents of the dermis. They represent about 70%of the dermis in dry weight. These fibers form vast and tough networks providing thedermis with strength, tension and elasticity. A collagen fiber is 0.5 to 3 μm in diameterand has a very long shape. A collagen fiber has a hierarchical structure [77] and isessentially a bundle of smaller microcables called collagen fibrils. Collagen fibrils are 10
to 300 nm in diameter and many micrometers long. A collagen fibril is a bundle of triple
stranded collagen molecules (about 1.5 nm in diameter [77, 78] and 300 nm long [78]),
three polypeptide chains that are wrapped around each other as a triple helix. The structureof collagen fibers starts to denature around the age of thirty. Photo-damaging, which
occurs with ultraviolet light in sunlight and due to smoking, also denature the structure ofcollagen fibers. These factors eventually cause morphological changes to the network ofcollagen fibers. This leads to loss of skin elasticity and finally induces wrinkling [79–83].
Elastin fibers are random coiled proteins that are also present in the dermis. Thesefibers are thinner than collagen bundles (1 to 3 μm in diameter [78]). They occupy 2 to
4% of the total weight of the dermis. An elastin fiber consists of two components–microfibrils and matrix elastin. The micro-fibrils are aggregated at the periphery of elastic fiber(10 to 12 nm thick) and are also present within elastin fibers as strands aligned along thelongitudinal direction (15 to 80 nm thick) [78]. Elastin fibers provide skin with elasticity
and resilience. Even though the volume of elastin fibers is much smaller than that ofcollagen fibers, elastin fibers also play an important role in providing structural support
to the dermis. Similar to collagen fibers, aging and ultraviolet light degrade elastin fibers,
which finally leads to wrinkling. Elastin fibers are extensible and return to their original
shapes after stretching. This property is not found in collagen fibers [78].

142.1.4 Chromophores
Skin includes various types of light-absorbing chemical compounds called
chromophores. Among these chromophores, melanin and hemoglobin are especially
important for understanding the appearance of normal skin since they absorb lightparticularly in the visible wavelength range [84, 85].
Melanin is the dominant chromophore of the epidermis. It can also be found inhair. Melanin is first produced in melanosomes, then is diffused into the epidermal layer,
and moves up towards the surface of skin while denaturing. Through this upward process,melanin changes its color from tan to white. Melanin is divided into two types, eumelaninand pheomelanin, depending on its chemical structure. Eumelanin is a black or darkbrown chromophore usually found in dark hair and eyes. Pheomelanin is yellow or
reddish brown chromophore that is observed in red hair and feathers. Usually, normal skincontains some amount of eumelanin. Therefore, in most studies on skin, “melanin” isreferred to as “eumelanin” [86]. The physiological function of melanin is to protect theinside of skin by absorbing and scattering ultraviolet light. When exposed to sunlight,
melanocytes start to produce melanin. This is the biological reaction that eventually
makes our skin appear tanned. The color of skin depends on the fraction of the volume of
the melanosomes. In the light colored skin of Caucasians, the fraction is only between 1
and 3%. In the skins of well-tanned Caucasians and Mediterraneans, the percentage
increases to 11 to 16%. In dark colored African skin, it goes up to 43% [86].
Hemoglobin is a red colored chromophore found in erythrocytes. Hemoglobin
represents 95% of the dry mass of an erythrocyte. Hemoglobin binds oxygen effectively
and carries oxygen to all body site through vessels and capillaries. When hemoglobin
contains oxygen, it is called oxy-hemoglobin. Otherwise, it is called deoxy-hemoglobin.

15

Usually, in the vein, more than 47% of the hemoglobin is oxy-hemoglobin [87]. Oxyhemoglobin is a brighter shade of red than deoxy-hemoglobin.

2.2 Constitution of Skin Tissue

2.2.1 Introduction
Skin is the largest body organ and forms the interface between the human bodyand the environment. Some of its functions are thermo-regulation, protection, metabolic
functions, and tactile sensitivity. Skin tissue varies in thickness according to body location
and wear-and-tear and this thickness variation contributes to the overall skin reflectance.
Skin represents about 12 to 16% of the total body mass and covers an area of about 1.2 to
2 m2 in adults [88]. Skin tissue is also different between men and women, being generally
less thick in women. The stratum corneum layer of the epidermis exhibits the greatest
variation in thickness: in thin skin there may be fewer than 10 cell layers in the stratum
corneum compared to over 100 layers in thick skin. The overall skin structure isconstructed from three main layers: the epidermis, dermis, and hypodermis, as shown in
Fig. 2.1. Each structure provides a distinct role in the overall function of the skin. Skin
reflectance is mainly determined by two pigments: melanin and hemoglobin. Skin optics
have been investigated by several authors in medicine, biology, and biophysics [86–93],
to name a few. As we will discuss, skin is a highly scattering media, i.e., scattering ismuch more important than absorption, and it also exhibits a strong forward scattering
behavior. Skin models are complicated by the fact that skin is irregularly shaped,
inhomogeneous, multi-layered, has anisotropic physical properties, and has hair follicles

16and glands [87]. The next sections provide descriptions of the anatomy, physiology, andoptics of the various skin layers. The interaction of light with skin plays a metabolic
function since the action of light on a precursor compound in the skin produces VitaminD which has a role in calcium and phosphate metabolism [94].
Cellular level elements form the three different skin layers: epidermis, dermis andsubcutaneous. These layers are composed of different types of cellular level elements.
Hence, they are very different in terms of structure and function. As a result, they exhibit
different types of light propagation.

Free nerve ending
Epidermis

Hair follicle

Small blood vessels

DermisHair root
Subcutaneous

Vein
ArteryAdipose tissue

Fig. 2.1. A cross-sectional schematic diagram of skin. Skin is a complex
multi-layered tissue consisting of various types of components,
including veins, capillaries, hairs, cells, fibers, etc.

17

2.2.2 Epidermis
The epidermis is the outermost layer of skin. There are no veins and capillaries inthis layer. Its thickness is about 0.2 mm on average and this thickness varies dependingon the location on the body. Furthermore, the thickness also varies according to thevolume of water that the epidermis holds.
The epidermis is further divided into five sublayers. From the bottom (innermost),
these sublayers are stratum basale (basal cell layer), stratum spinosum (prickle cell layer),
stratum granulosum (granular cell layer), stratum lucidum (clear layer) and stratum
corneum (horny cell layer).
The epidermis is a metabolically active tissue. Keratinocytes produced in stratum
basale move upward to the outer surface. This process is called turn-over. During thisturn-over, keratinocytes change their structures and physiological functions. One cycle ofthis turn-over process takes about 28 days. In the following, we will describe the physioanatomical properties of each sublayer.
Stratum basale (basal cell layer) is the deepest sublayer of the epidermis and iscomposed of a single layer of basal cells. This sublayer forms the boundary to the dermis.
Keratinocytes are produced in this sublayer. It holds approximately 8% of the water in theepidermis. With aging, this layer becomes thinner and loses the ability to retain water.
Melanocytes, which were mentioned in the previous section, also lie in this layer.
Stratum spinosum (prickle cell layer) refers to the 10 to 20 layers that lie on top of thebasal cell layer. Basal cells, through the process of turn-over, make their shape somewhat
flatter (multi-sided) and form these layers. These cells are called prickle cells and havelittle spines on the outside of their membrane. The thickness of this sublayer is typicallyfrom 50 to 150 μm [84].

18Stratum granulosum (granular cell layer) is composed of 2 to 4 granular cell layers.
The typical thickness is 3 μm [84]. In this sublayer, cornification called keratinization of
keratinocytes begins. In this process, organelles such as nuclei and mitochondria start toresolve. Cells are increasingly filled with keratin fibers and contain less moisture thanbasal and prickle cell layers. The shape of these cells becomes much flatter during thisprocess.
Stratum lucidum (clear layer) can only be found in soles and palms. It is a highly
refractive sublayer. Its cells become flatter and more densely packed during turn-over.
Stratum corneum (horny cell layer) is the exterior sublayer of the epidermis. Its
thickness ranges from 8 to 15 μm [84]. This sublayer is composed of several layers ofhexagonal-shaped flat and hard cells named horny cells or corneocytes. These are drydead cells without organelles and filled with keratin fibers. This sublayer prevents
excessive dehydration of the skin tissue and usually contains 10 to 15% of the mass ofwater in the epidermis, depending on the skin condition. Horny cells are surrounded byintercellular lipids. A principal constituent is ceramide, which plays a crucial role in water
retention [95]. Horny cells also contain special chemical compounds called natural
moisturizing factor (NMF) that also plays an important role in retaining skin moisture.
NMF is composed of sodium PCA, sphingolipids and ceramides, phospholipids, fatty
acids, glycerol, squalane and cholesterol [95]. Skin that lacks NMF and ceramide tends
to be very dry.

2.2.3 Dermis
The dermis is the second layer of skin, beneath the epidermal layer. This layer ismuch thicker than the epidermis (usually 1 to 4 mm [84]). The main components of the19dermis are collagen and elastin fibers. Compared to the epidermis, there are much fewer
cells and much more fibers in the dermis. Dermis has the following two sublayers.
Papillary layer is the upper sublayer of the dermis that is clearly demarcated fromthe epidermis. This sublayer is a loosely connected tissue and includes a large amount of
nerve fibers, capillaries, water and cells (e.g fibroblasts). In this sublayer, collagen fibers
form a finer network than those of the reticular layer [96].
Reticular layer constitutes the lower part of the dermis and represents a continuoustransition to the subcutis. This sublayer has a denser and thicker network than thepapillary layer and includes fewer nerve fibers and capillaries. In this sublayer, collagenfibers are aggregated into thick bundles which are mostly aligned parallel to the surface
of skin [96].
The micro-anatomical complexity of skin, particularly in cell level, makes
quantitative analysis of the optical properties of skin difficult. However, it can besignificantly simplified by considering the physio-anatomical characteristics of each skinlayers. As mentioned above, the epidermis and the dermis are very different incomposition, thickness and functions. Hence, these two layers can be considered to be
independent of each other in terms of optical behaviors. Indeed, the epidermis and thedermis are viewed as independent optical media in many early studies on skin optics. For
example, melanin is present only in the epidermis. On the other hand, hemoglobin isfound only in the dermis since there are no veins and capillaries in the epidermis. Hence,the epidermis can be essentially viewed as a melanin layer and the dermis can be viewed
as a hemoglobin layer when analyzing the absorption properties of skin.

20

2.2.4 Subcutaneous Tissue
Subcutaneous tissue, or hypodermis in histology, is the third layer beneath thedermis. It is important to note that it is not categorized as another skin layer. Subcutaneous
tissue is an elastic layer and includes a large amount of fat cells that work as a shock
absorber for blood vessels and nerve endings. The thickness of this layer is reported to be
4 to 9 mm on average. However, the actual thickness differs from person to person andalso depends on the body region.

2.2.5 Skin Layers
Skin is mainly composed of skin layers, fine wrinkles, hairs and skin surface lipids.
Skin layers are the main component of skin. Fine wrinkles, hairs and skin surface lipids
are observed on the outermost surface of skin. These components exhibit very different
optical behaviors according to their structures. Skin layers usually consist of two different
layers, the epidermis and the dermis. There is a very clear wavy boundary between these
two layers. The thickness of the skin layers differs quite a bit depending on gender, age,
individual, body regions, etc. It has been found that males tend to have thicker skin layers
than females [97]. On the other hand, while several studies on the relation between age
and the thickness of the skin layers have been reported [98–100], a clear relationship has
not yet been found. Conditions of skin such as water-retention also differ depending on
region, age and individual. For instance, skin layers of older people have less ability forwater-retention, since the NMF tends to decrease with age. It has also been reported thatthe transparency of the stratum corneum (the outermost sublayer of the epidermis)
decreases with the amount of water contained in it [101].

21

2.2.6 Hairs
Hairs behave as strong scatterers of light and affect the surface reflection of skin.
Hairs are distributed all over the surface of the body except for the palms of the hands,
the soles of the feet, the tops of the feet and mucocutaneous junctions [102, 103]. Hair
grows from a hair follicle which resides in the dermal layer and opens onto the surface ofskin. The bottom end of a hair follicle sits close to the boundary between the dermis andthe subcutis. The color of hair is determined by the amount of eumelanin (dark brown) or
pheomelanin (yellow reddish brown) it contains. Hair that does not contain melanin iswhite.
Hair can be categorized into two types – terminal thick hair and vellus fine hair.
Terminal hair is usually hard and long. Examples include, the hair on the head, eyelashes,and eyebrows. Vellus hairs, sometimes called “peachy fuzz”, are soft and short(approximately 1 mm long). They can be found on the cheek, forehead, arms, etc. In body
regions that are covered with vellus hair, the hair plays an important role in the visualappearance of the region.

2.2.7 Skin Surface Lipid
Skin surface lipid plays an important role in surface reflection from skin. It reflects
incident light specularly and makes skin appearance shinier. Skin surface lipid can beobserved in most body regions, but its volume varies from one region to another.
Furthermore, it also varies with respect to gender, age, etc.
Skin surface lipid forms a thin film called the skin surface lipid film (SSLF) on
skin layers. The SSLF protects excess evaporation of water from skin and keeps skinmoisturized and smooth. The basis of the SSLF is sebum, which is a yellowish oily liquid22secreted by sebaceous glands. Sebaceous glands are present in the dermal layer and areconnected to hair follicles. They cover a large region of the face, the middle of the backand the chest [102]. Although the contents of sebum vary depending on the body region,
the composition of sebum is roughly estimated to be 30% of free fatty acid, 33% of
triglycerides, 15% of wax, 5% of sterol esters, 5% of squalene and 7% of paraffin [104].
After sebum is secreted, it gets mixed with sweat and lipid. This results in an emulsified
film over the surface of skin [104]. The SSLF spreads over the skin surface as an unevenly
distributed amorphous sheet. The thickness of the SSLF varies from one body region toanother and is roughly estimated to range from 0.01 to 2.1 μm [104]. The degree of sebum
secretion depends on age, gender, body regions, etc. Sebum secretion starts to decrease
from the mid-twenties and at the age of fifty becomes similar in degree to that in the caseof a ten years old [104].

2.3 Optical Propagation Theory in Biological Tissue

2.3.1 Spectral Reflectance and Transmittance
The reflection is the return of radiation intensity by a surface, without a change inwavelength.

The reflection is usually divided into two components, the specularreflection, in which the angle of incidence is equal to the angle of reflection, and diffuse
reflection, in which the angle of incidence is not equal to the angle of reflection. Everysurface returns both specular and diffuse reflections. Some surfaces may return mostly
specular reflection, others more diffuse reflection. The glossier the surface, the more
specular the reflection. Spectral reflectance is the ratio of the spectral radiant intensity or

23

luminous flux reflected in a given cone, whose apex is on the surface considered, to thatreflected in the same directions by the perfect diffuser identically irradiated, and isexpressed asR( ) I s ( )  I D (  ),I R ( )  I D ( )(2.1)

where IS is the sample intensity at wavelength , ID is the dark intensity at wavelength,IR is the reference intensity at wavelength .
The transmission is the percentage of the radiant intensity passing through asystem considered relative to the amount that passes through the reference (such as air).
The spectral transmittance is the ratio of the spectral transmitted radiant intensity or
luminous flux to the incident flux under specified conditions of irradiation, is expressedas
T ( ) I s ( )  I D ( ),I R ( )  I D ( )(2.2)

where IS is the sample intensity at wavelength , ID is the dark intensity at wavelength,IR is the reference intensity at wavelength .

2.3.2 Lambert-Beer’s Law
The absorbance spectrum A() is a measure of how light is absorbed by aabsorbing sample such as a solution. The absorbance spectrum of a solution is related tothe concentration of the absorber within it. This relationship is known as the LambertBeer law, and is given as
A( )  log10 (1/ T ( ))   ( )  C  l ,(2.3)

where T() is the transmittance spectrum, () is the molar extinction coefficient of24

absorbers at wavelength , C is the molar concentration of absorber, and l is the optical
pathlength.

2.3.3 Spectrocolorimetry
Spectrocolorimetry [105, 106] is a typical method for calculating the colorimetricvalue from the measured spectral power, reflectance, or transmittance. The most popularcolor system is the CIEXYZ color system [105, 106], which recommended byCommission Internationale de l’Éclairage, CIE. In this color system, the tristimulus
values, X, Y, and Z corresponding to so-called R (red), G (green), and B (blue),respectively, are treated as the colorimetric values. For the surface color of an object,these tristimulus values are expressed as

X  K  S ( )  x ( )  R( ) ,(2.4)

Y  K  S ( )  y ( )  R( ) ,(2.5)

Z  K  S ( )  z ( )  R( ) ,(2.6)

where S() and R() denote the spectral power distribution of illuminant and spectralreflectance of object, respectively.

x ( ) , y ( ) , and z () are the color matching

functions [105, 106] as shown in Fig. 2.2. The values of the constant K which results in
Y being equal to 100 for the perfect diffuser is given byK  100 /  S ( )  y ( ) .

(2.7)

In Eqs. (2.4)-(2.7), the summation can be carried out using data at every 20 nm, from 400
to 700 nm. For the most applications, it is necessary to take values either at every 10 nm,
or preferably at every 5 nm, and use a rage of wavelengths from 380 to 780 nm.
The retina of human eye varies considerably in its properties from one point to25

another, and matches if the field size is altered. If the field size is reduced, the ability todiscriminate one color from another becomes less marked. Hence, 2º matches tend to
break down. For this reason, CIE recommended a different set of color matching
functions for the samples having field sizes greater than 4º. These supplementary color
matching functions, x10 ( ) , y10 ( ) , and z10 ( ) , are also shown in Fig. 2.2. It can beseen that the two sets of functions are similar, but the differences are large enough to besignificant. The x10 ( ) , y10 ( ) , and z10 ( ) functions can be used as weighting functions
to obtain the tristimulus values X10, Y10, and Z10, by means of procedures analogous tothose adopted to obtain X, Y, and Z. All measures are distinguished by the presence of a
subscript 10. The tristimulus value Y represents the percentage luminance factor, and thisis an approximate correlation of the perceptual attribute of lightness. The values thenrange from 100 for white, or transparent objects, that absorb no light, to zero for objects
that absorb all the light. It is sometimes convenient to use the ratio Y/Yn, where Yn is thevalue of Y for a suitable chosen reference white or reference transparent specimen. If thereference object absorbs some light, as is usually the case, Y/Yn will be slightly more that
1 for the perfect diffuser.
The CIE x-y chromaticity diagram and the CIE 1976 uniform chromaticity scale
diagram have many uses. However, as they show only proportions of the tristimulus
values and not their actual magnitudes, they are only strictly spplicable to color all having
the same luminance. In general, colors differ in both chromaticity and luminance, andtherefore, some method of combining these variables is required. To meet this need forluminance factor (but not for luminance), the CIE has recommended the use of one of twoalternative color spaces, CIELUV color space and CIELAB color space [105, 106]. Here,
only CIELAB color space shown in Fig. 2.3 is described. It is produced by plotting along26

three axes at right angles to one another as,

1

L  116(Y / Yn ) 3 16
L  903.3(Y / Yn )for Y / Yn >0.008856 ,for Y / Yn  0.008856 ,a  500 ( X / X n ) 3  (Y / Yn ) 3  ,


1

1

(2.9)
(2.10)b  200 (Y / Yn ) 3  (Z / Z n ) 3  ,


1

(2.8)

1

(2.11)

where Xn, Yn, and Zn are the tristimulus value of perfect diffuser, and is equal to or lessthan 0.008856.
The CIELAB color space is intended to apply to object colors of the same sizeand shape, viewed in identical white to mid-gray surroundings, by an observer
photopically adapted to a field of chromaticity not too different from that of average
daylight. If the samples considered have an angular substance greater than 4º, then X10,
Y10, and Z10 should be used instead of X, Y, and Z.
If the differences between two colors in L , a , and b , respectively, the total
color difference  Eab
may be evaluated as1

 Eab  ( L ) 2  (a ) 2  ( b ) 2  2 .

(2.12)

Thus, the value of  Eab
is equal to the distance between the two points representing thecolor in the CIELAB color space.

27

2.2

Spectral tristimulus values

2.0

x
y
z
x10

_
z ( )

1.8
1.6

y10

_
y ( )

1.4

z10

1.2

_
x ()

1.0
0.8
0.6
0.4
0.2
0.0
400

450

500

550

600

650

Wavelength (nm)Fig.2.2. Color matching functions.

28

700

750

Fig.2.3. A three dimensional representation of the CIE LAB color space.

29

2.3.4 Photon Fluence
Photon fluence ϕ [11] represents the amount of light absorption in each volume
element of skin tissue model, and is calculated by,
𝜙[𝑖𝑥 , 𝑖𝑦 , 𝑖𝑧 ] =𝐴[𝑖𝑥 , 𝑖𝑦 , 𝑖𝑧 ][cm−2 ]
µ𝑎(2.13)

Where A denotes the probability density of amount of light absorption per unit volume
with µa being the local absorption coefficient, and ix, iy and iz indicate the element numberof each grid in the horizontal (ix, iy) and vertical (iz) direction in the skin tissue model.
The fluence is usually resolved along the depth (iz) and such examples are shown in Fig.
2.4, where photon fluence is mapped along x-z plane, and x direction represents skinsurface and z direction depth of tissue model. The fluence near the photon incident has alarger value due to the addition of back scatter light. Also, medium with refractive index
mismatched boundary has higher fluence rate because of internal reflection compared torefractive index matched boundary (detailed explanation is given in chapter 3). Two typesof flucence calculation are used in this study. One is a fluence map of the total components
which consist of all the reflected, absorbed and transmitted components and the otherfluence map is of the detected components consisting only reflected components, as

51

104

01

5

0

(a) Fluence of the total components.

10-4(b) Fluence of the detected components.

Fig.2.4. Two typical maps of simulated photon fluence along z direction.

30

Photon fluence [cm-2]

shown in Fig. 2.4.

2.3.5 Point Spread Function of Reflected Intensity
When an infintely narrow photon beam incident on the surfacr of the skin tissue
model, it strats propagating inside the skin model with random steps in random direction.
After the endo of propagation, if the backscattered photons are recorded on the skinsurface, it generates an intensity decaying map which has maximum intensitynear the
point of incident. From this intensity map, point spread function (PSF) of reflected
intensity on the skin surface can be calculated. Typical examples of simulated PSF at
400nm, 550nm and 700nm are shown in Fig. 2.5, in which x= 0 means center of point
like illumination. PSF has wavelength dependency because of varying optical path length
in tissue. Owing to this property PSF of longer wavelength (700nm) shows broaddening
nature and is suitable for evaluating change of skin condition in deeper layers, as can beseen in Fig. 2.5(c) (detailed explanation is given in chapter 4).

(a) At 400nm(b) At 550nm(c) At 700nm
Fig.2.5. Typical examples of simulated PSF at three different wavelengths.

31

2.4 Multiple Regression Analysis
In the multiple regression analysis [107], a relationship between two variable setsyk (k =1, 2, …, p) and xjk (j =1, 2, …, q) is assumed to be given by the following linear
equation,qyk   0   j  x jk  ek ,(2.14)

j 1

where yk and x jk represent response variables and predictor variables, respectively,  0means a constant component, and ek expresses unknown error components. In thismultiple regression model, p is the number of sample data being analyzed and q is that ofpredictor variables. The regression coefficients  j (j  0, 1, 2,, q) are unknown values

that are estimated by the method of least squares in the analysis. Let us denote theestimated regression coefficients by ˆ j , then the corresponding multiple regression
equation is expressed asqyˆ k  ˆ 0  ˆ j  x jk ,(2.15)yk  yˆ k  rk

(2.16)

j 1

where yˆ k means the response variables estimated with using ˆ j , and rk is the residual.
A measure for suitability of the regression equation is given by the parameter P2, which
is defined as the square of the multiple correlation coefficient P, that is,


2
P 



2


 k  yk  yk  yˆ k  yˆ k

,
2 
2
 k  yk  yk   k yˆ k  yˆ k 









(2.17)

where yk and yˆ k denote the average of yk and yˆ k , respectively, over the p number of32

data. The value of P means the correlation coefficient between yk and yˆ k . The value ofP2 ranges from 0 to 1, and the linear approximation becomes better as P 2 approaches to1.

2.5 Monte Carlo Modeling of Light Transport in Scattering Medium

2.5.1 Optical Properties
The simple concept of absorption of light can be explained as following
description. Consider a chromophore idealized as a sphere with a particular geometricalsize. Consider that this sphere blocks incident light and casts a shadow, which constitutes
absorption. The size of the absorption shadow is called the effective cross-section, a(cm2), and can be smaller or larger than the geometrical size of the chromophore, A (cm2),
related by the proportionality constant called the absorption efficiency, Qa(dimensionless), as, a (cm 2 )  Qa (-)  Aa (cm 2 ) .

(2.18)

The absorption coefficient a (cm-1) describes a medium containing many chromophores
at a concentration described as a volume density, a (cm3). The absorption coefficient isessentially the cross-sectional area per unit volume of medium.

 a (cm -1 )   a (cm -3 )   a (cm 2 ) .

(2.19)

Experimentally, the units (cm-1) for a are inverse length, such that the product a  L is
dimensionless, where L (cm) is a photon’s path length of travel thorough the medium.
The probability of survival or transmission T of the photon after a path length L is:33

T  exp( a  L) .

(2.20)

This expression for survival holds true regardless of whether the photon path is a straightline or a highly tortuous path due to multiple scattering in an optically turbid medium.
The simple concept of scattering of light can be explained as following
description.

Consider a scattering particle idealized as a sphere with a particulargeometrical size. Consider that this sphere redirects incident photons into new directions
and so prevents the forward on-axis transmission of photons, thereby casting a shadow.
This process constitutes scattering. The size of the scattering shadow is called theeffective cross-section, s (cm2), and can be smaller or larger than the geometrical size ofthe scattering particle, A (cm2), related by the proportionality constant called thescattering efficiency, Qs (dimensionless), as, s (cm 2 )  Qs (-)  As (cm 2 ) .

(2.21)The scattering coefficient s (cm-1) describes a medium containing many scattering
particles at a concentration described as a volume density, s (cm3). The scattering
coefficient is essentially the cross-sectional area per unit volume of medium.

 s (cm -1 )   s (cm -3 )   s (cm 2 ) .

(2.22)

Experimentally, the units (cm-1) for s are inverse length, such that the product  s  L is
dimensionless, where L (cm) is a photon’s path length of travel thorough the medium.
The probability of transmission T of the photon without redirection by scattering after apath length L is:
T  exp(  s  L)

(2.23)

Biological tissue can be assumed to be a random turbid medium, with variations

34

in the optical properties small enough to prevent localized absorption. In other words,
tissue is considered to have volumetric scattering and absorption properties rather thanbeing composed of discrete scattering and absorption centers distributed in a nonscattering, non-absorbing medium. The advantage to the distributed scattering center
approach is that for perfect spheres the phase function is known, however there is little
similarity between perfect spheres and tissue. Volumetric absorption or scattering is
obtained by multiplying an absorption or scattering cross section with the density ofabsorbers or scatterers [108]. This is how the absorption coefficient a and scattering
coefficient s are defined.

The scattering and absorption coefficient are typicallymeasured in inverse centimeters and the reciprocal of these coefficients is the average
distance that light will travel before being scattered or absorbed, respectively.
The anisotropy factor g (dimensionless) is a measure of the amount of forward
direction retained after a single scattering event. Considering that a photon is scatteredby a particle so that its trajectory is deflected by a deflection angle , as shown in Figure
2.6. Then the component of the new trajectory which is aligned in the forward direction
is shown in a dash line as cos(). On average, there is an average deflection angle andthe mean value of cos () is defined as the anisotropy. A scattering event causes a
deflection at angle from the original forward trajectory. There is also an azimuthal angleof scattering, . But it is the deflection angle  which affects the amount of forward
direction, cos, retained by the photon. Consider an experiment in which a laser beam
strikes a target such as a cylindrical cuvette containing a dilute solution of scattering
particles. The scattering phase function p() is measured by a detector that is moved ina circle around the target while always facing the target. Hence the detector collects light35

scattered at various deflection angle in a horizontal plane expectation value for cos():g   p( ) cos 2 sin  d  cos ,(2.24)0where p( )2 sin d  1.

(2.25)

0

It is common to express the definition of anisotropy in an equivalent way:1g   p (cos  ) cos  d (cos  ),(2.26)

-1

where
1

 p(cos ) d (cos )  1.

-1

36

(2.27)

Deflection
angle, Photontrajectry

Azimuthalangle, cos (
Scattering
event

Fig.2.6. Schematic of the definition of anisotropy factor g. A scattering event
causes a deflection at angle  from the original forward trajectory.
There is also an azimuthal angle of scattering, . However, it is thedeflection angle  which affects the amount of forward direction, cos(), retained by the photon.

37

2.5.2 Scattering Phase Function
The angular dependence of scattering is called the scattering phase function, p()which has units of [sr -1] and describes the probability of photon scattering into a unit
solid angle oriented at an angle relative to the photons original trajectory. Note that thefunction depends only on the deflection angle and not on the azimuthal angle . Suchazimuthally symmetric scattering is special case, but is usually adopted when discussing
scattering. However, it is possible to consider scattering which does not exhibit azimuthal
symmetry.
The simplest scattering phase function is the isotropic phase function:p( ) 1.
4(2.28)The isotropic phase function would scatter light with equal efficiency into all possible
directions.
The Henyey-Greenstein scattering phase function [109] has proven to be useful inapproximating the angular scattering dependence of single scattering events in biologicaltissues. The Henyey-Greenstein scattering phase function allows the anisotropy factor g
to specify p() such that calculation of the expectation value for cos() returns exactly
the same value of g. This scattering phase function is given as,p( ) 1
4

1 g 2

1+g  2g cos 232(2.29).

It is common practice to express the Henyey-Greenstein function as the function p(cos);p(cos  ) 1
2

1 g 2

1+g  2g cos 

38

2

3
2

.

(2.30)

2.5.3 Monte Carlo Method
Monte Carlo method [10, 11] has been used to simulate the photon migration in
the random scattering media such as a biological tissue. In all applications of the Monte
Carlo method, a stochastic model is constructed in which the expected value of a certain
random variable is equivalent to the value of a physical quantity to be determined. The
Monte Carlo method describes local rules of photon propagation that are expressed, inthe simplest case, as probability distributions that describes the step size of photon
movement between sites of photon-tissue interaction, and the angles of deflection in aphoton’s trajectory when scattering event occurs. Since, the Monte Carlo method isstatistical in nature and, thus, relies on calculating the propagation of a large number ofphotons by the computer, the method requires a large amount of computational time.
The Monte Carlo simulations are based on the macroscopic optical properties thatare assumed extend uniformly over small units of tissue volume. In this dissertation, thephotons are treated as classical particles, and polarization and wave phenomenon are
neglected. Once photon which has the weight w launched in the tissue, it is moved a step
size s where it may be scattered, absorbed, internally reflected, or transmitted out of themedium. The photon is repeatedly moved until it either escapes from or is absorbed
completely in medium. This process is repeated until the desired number of photons has
been propagated.

The physical quantities such as the diffuse reflectance, totaltransmittance, and internal absorption are scored during the simulation. In this section,
the algorithms of Monte Carlo simulation reported by Prahl et al. [10], and Wang et al.
[11], are mainly referred.

39

A.

Coordinate Systems
The simplest Monte Carlo simulation deals with the transport of an infinitely

narrow photon beam, perpendicularly incident on a simple- or multi-layered tissue. Eachlayer is infinitely wide, and is described by the following parameters: the scattering
coefficient s(cm-1), absorption coefficienta(cm-1), anisotropy factor g, refractive index
n, and thickness t (cm). The refractive indices of the ambient medium above the tissue(e.g. air) and the ambient medium below the tissue (if existing) need to be given as well.
The layers are parallel to each other. For the simplicity of notation, the total interaction
coefficient t, which is the sum of the scattering coefficient s and the absorption
coefficient a, is sometimes used in this dissertation. The interaction coefficient means
the probability of photon interaction per unit infinitesimal path length.
Three coordinate systems shown in Fig.2.7 are used in the Monte Carlo simulation[10, 11] at the same time. A Cartesian coordinate system is used to trace photon
movements. The origin of the coordinate system is the photon incident point on the tissue
surface; the z-axis is the normal of the surface pointing toward the inside of the tissue;
and the xy-plane is on the tissue surface.
As the infinitely narrow photon beam is perpendicular to the tissue surface, theproblem has cylindrical symmetry. Therefore, a cylindrical coordinate system is set up
to score internal photon absorption as a function of radial r and z. The cylindricalcoordinate system share and Cartesian coordinate system share the origin and z-axis. Ther coordinate of cylindrical coordinate system is also utilized for the diffuse reflectanceand diffuse transmittance as a function of r and , where  is the angle between the photon
exiting direction and the normal (-z-axis and z-axis) to the tissue surface.

40A moving spherical coordinate system, whose z-axis is dynamically aligned with thephoton propagation direction, is used for sampling of the propagation direction change ofa photon. In this spherical coordinate system, the deflection angle and the azimuthal
angle  due to scattering are first sampled. Then, the photon direction is update in termsof the directional cosines in the Cartesian coordinate system.

41

x
Old photon position (x, y,
z)

(a)

y
x

y

Propagation
distance, szIncident
photonEscapephoton

New photon position (x’, y’, z’)z(b) Cartesian coordinate systemrxz(c) Cylindrical coordinate system

New photon
direction(x ’, y ’,
z’)

Scattering pointy
Old photon
direction(x , y , z)

z





(d) Spherical coordinate system

Fig.2.7. The three coordinate systems used in the Monte Carlo simulation.

42

B.

Launch of the Photon
The location of a photon is described by the Cartesian coordinates (x, y, z). The

propagating direction of a photon is described by the directional cosines (x, y, z). Eachphoton is initially assigned a weight w, equal to unity. The photon is injected orthogonallyonto the tissue at the origin. The photon position (x, y, z) is initialized to (0, 0, 0), anddirectional cosines (x, y, z) are se to be (0, 0, 1). The weight w is initialized to be 1.
Once the photon is launched, if there is a refractive-index-mismatched interface
between the tissue and ambient medium, some specular reflectance will occur. When therefractive indices of the ambient medium and tissue are n1 and n2, respectively, thespecular reflectance, Rsp, is specified as,
Rsp (n1  n2 ) 2.
(n1  n2 ) 2(2.31)

If the first layer is clear medium, which is on top of layer of medium whoserefractive index is n3, multiple reflections and transmissions on the twp boundaries of theclear layer are considered. The specular reflectance in this case is calculated as,(1  r1 ) 2  r2
Rsp  r1 
1  r1  r2

(2.32)

where r1 and r2 are the Fresnel reflectance on the two boundaries of the clear layer:r1 ( n1  n2 ) 2,( n1  n2 ) 2(2.33)

r2 (n3  n2 ) 2.
(n3  n2 ) 2(2.34)

After the specular reflection, the photon weight initialized to 1 is decreased by Rsp for the43photon to enter the tissue asw  1  Rsp .

C.

(2.35)

Moving of the Photon
The step size s represents the distance between two successive interactions, orscattering in the tissue. The step size for each photon step s follows Beer’s law, that is
to say, it is more likely for a photon to travel a short distance than a long distance and theprobability is proportional to exp( t  Δs) . A function of a random variable ()uniformly distributed between 0 and 1, which yields a random variable with this
distribution [10, 11] iss  ln 

t.

(2.36)

Once a step size s is determined, the photon is moved in the tissue. The positionof photon is update by:x  x   x  sy  y   y  s ,(2.37)z  z   z  s

where the arrows indicate quantity substitutions. The variables on the left-hand side havethe new updated values, and the variables on the right-hand side have the old values.

44

D.

Absorption of the PhotonOnce the photon has reached an interaction site, a fraction of the photon weight(energy of photon) w, absorbed by the interaction site is calculated as a w , t 

w  

(2.38)

which will be deposited in the local grid element. The photon weight w is scored intoA(r, z) at the local grid element:A(r, z)  A(r, z)  w .

(2.39)

The current photon weight is updated as well by:w  w  w .

(2.40)The photon with new weight w will suffer scattering at the interaction site.

E.

Scattering of the PhotonOnce the photon has reached an interaction site and its weight decreased, thephoton with new weight is ready to be scattered. There will be a deflection angle,

 (0     ) , and an azimuthal angle,  (0    2 ) , to be sampled statistically. The
probability distribution for the cosine of the deflection angle, cos, is dscribed by thescattering phase function. When the Henyey-Greenstein function [110] is employed, thescattering phase function is described as
1 g 2p(cos  ) 

2(1+g  2g cos  )
2

3
2

,

(2.41)

where the anisotropy, g, equals  cos  and has a value between –1 and 1. A value of 0
indicates isotropic scattering and a value near 1 indicates forward-directed scattering. The

45

cos is expressed as a function of the random number, :cos   1  g 2  
1 
2
1

g



  if g  0 ,2g 
1  g  2g 2  

(2.42)cos  2 1(2.43)

if g  0 .

The azimuthal angle, , is uniformly distributed over the interval 0 to 2, is sampled as  2 3 .

(2.44)

Once the deflection and azimuthal angles are chosen, the new direction of the photon canbe calculated [10, 11] as x  y sin 
1   x2sin 
1   x2

(  x   z cos   y sin )   x cos(  y   z cos   x sin )   y cos .

(2.45)

 z   sin  cos 1   z2   z cosIf the photon direction is sufficiently close to the z-axis (e.g.  z  0.99999 ), then thefollowing formulas should be used: x  sin  cos y  sin  sin .
 z 

F.

(2.46)zcos
zInternal Reflection of Photon
The internal reflection may occur when the photon in the current layer is across aboundary into another layer with a different index of refraction. The possibility that thephoton will be internally reflected is determined by the Fresnel’s formulas [111, 112]:46

R(i ) 1  sin 2 (i  t ) tan 2 (i  t ) ,2  sin 2 (i  t ) tan 2 (i  t ) (2.47)

where i  cos-1 (  z ) is the angle of incidence on the boundary and the angle oftransmission  t is given by Snell’s law asni sin i  nt sin t ,(2.48)

where ni and nt are the indices of refraction of the medium from which the photon isincident and transmitted, respectively. A random number 4 uniformly distributed
between 0 and 1 is used to decide whether the photon is reflected or transmitted, that is,If  4  R(i ) , then the photon is internally reflected.

(2.49)

If  4  R(i ) , then the photon transmits.

(2.50)

If the photon is internally reflected, the photon stays on the boundary and its directional
cosines (x, y, z) are updated by reversing the z component:( x ,  y ,  z )  ( x ,  y ,   z ) .

(2.51)

If the photon transmits across the boundary, it is determined whether the photon hasentered another layer of tissue or ambient medium. If the photon is transmitted to anotherlayer of tissue, it continues propagation with an updated direction and step size. The new
directional cosines are given as x  sin  t   x / sin  i y  sin  t   y / sin  i z 
or employing Snell’s law, Eq.(2.52) rewritten as47

zcos  tz

(2.52)

 x   x  ni / nt y   y  ni / nt z 

(2.53)zcos  tz

G. Scoring Physical Quantities
In this section, two scored physical quantities; diffuse reflectance R andtransmittance T are described. The other quantities such as the internal absorption andfluence rate are excluded because this dissertation does not treat these quantities. If thephoton escapes the tissue into the ambient medium, the photon weight is scored into
diffuse reflectance or transmittance. If the photon has not been scattered, the photon
weight is scored into unscattered reflectance or transmittance depending on the where thephoton escapes. If the photon has been scattered at least once, the diffuse reflectance, R(r, t), or diffuse transmittance, T (r, t), at particular grid element (r, t) is incremented
by the amount of escapes photon weight, W:
R(r ,  t )  R(r ,  t )  w

if z  0

T (r ,  t )  T (r ,  t )  w

if z  the bottom of the tissue.

(2.54)

Because the photon has completely escaped, the tracing of this photon ends here. A new
photon may be launched into the tissue and traced thereafter. After tracing multiple
photons, the total diffuse reflectance R and transmittance T are calculated from the arrayof R (r, t) and T (r, t). They are represented by Rr[ir, i] and Tr[ir, i], respectively.
The coordinates of the center of a grid element are calculated by:r  (ir  0.5)  r (cm) ,48(2.55)  (i  0.5)   (rad) ,(2.56)

where ir and i are the indices for r and , respectively. The raw data give the total photon
weight in each grid element in the two-dimensional grid system. To obtain the total
photon weight in the grid elements in each direction of the two-dimensional grid system,the 2D arrays are summed in the other dimension:N 1

Rr [i r ]   Rr [i r , i ] ,(2.57)

i 0

Nr 1

R [i ]   Rr [i r , i ] ,(2.58)

ir 0

N 1

Tr [i r ]   Tr [i r , i ] ,(2.59)i 0

N r 1T [i ]   Tr [i r , i ] .

(2.60)

ir 0

To obtain the raw data of total diffuse reflectance and transmittance, the 1D arrays are
summed again:N r 1R   Rr [i r ] ,(2.61)ir 0

N r 1T   Tr [i r ] .

(2.62)

ir 0

Finally, the total diffuse reflectance and transmittance are obtained by dividing the raw
total diffuse reflectance and transmittance by the total number of photons as

R  R/ N[] ,(2.63)

T T / N[] ,(2.64)

where [-] indicates dimensionless units.

49

2.5.4 Inverse Monte Carlo Method
As mentioned section 2.5.3, the Monte Carlo method is generally used for
simulation of measurable physical quantities such as the reflectance, transmittance, andinternal absorption using given parameters. On the other hand, it can also be a means toestimate those parameters of the tissue such as the absorption coefficient a, scattering
coefficient s, and anisotropy factor g, from the measured quantities such as the measured
reflectance, transmittance, and/or collimated transmittance. This approach is called theinverse Monte Carlo method [88,113]. In the inverse Monte Carlo method, to determine
the unknown optical parameters from measurable macroscopic values, the Monte Carlosimulation is inverted. First of all, the Monte Carlo simulation taking the set of initialvalues for the optical parameters a, s, and/or g calculates the reflectance, transmittance,
and/or collimated transmittance. Then, the calculated quantities are compared with themeasured ones. If the significance deviations, the optical parameters are varied slightly,
and the new forward simulation are performed. This process is repeated until thedeviation between measured and calculated values is within the predetermined threshold.

2.6 Optical Properties of Skin Tissue Components

2.6.1 Scattering by Tissue
The light scattered by a tissue has interacted with the ultrastructure of the tissue.
The tissue ultrastructure extends from membranes to membrane aggregates to collagenfibers to nuclei to cell. The light are most strongly scattered by those structures whose50

size matches the wavelength of light. For the human skin tissue, the major scatters are
the keratin fibers of epidermis, collagen fibers of dermis, and red blood cell, or
erythrocyte in blood. The epidermis with its keratin fibers appears to behave somewhat
like dermis with collagen fibers, and the scattering coefficient of epidermis is tentatively
approximated by that of dermis [7, 88]. The scattering coefficient of dermis have beenstudied theoretically, as the combination of Mie and Rayleigh scattering primarily fromthe collagen fibers [88]. The scattering of blood is mainly caused by a refractive index
mismatch between the hemoglobin solution within the erythrocyte and the surrounding
plasma. In the visible to near-infrared wavelength region, thetypicalvaluesofanisotropy scattering parameter g of the skin tissue are in the range of 0.7-0.95, while thatof red blood cell in human blood is the range of 0.985-0.995 [111,113,114]. These values
vary with wavelength. Fig. 2.8 shows the published values of (a) scattering coefficientof epidermis and dermis, (b) scattering coefficient of blood, (c) anisotropy scatteringparameter of epidermis and dermis, and (d) anisotropy factor of blood.

2.6.2 Absorption by Biological Chromophores
Molecules that absorb light are called chromophores. The major biologicalchromophores in the human skin tissue are melanin and hemoglobin. Melanin is apolymer built by condensation of tyrosine molecules and usually found in melanosome atbasal layer of epidermis. Melanin has a broad absorption spectrum exhibiting stronger
absorption at shorter wavelengths [115-117] as shown in Fig.2.9. Melanosomes may
contain a variable amount of melanin. The average epidermal absorption coefficientdepends on both the absorption coefficient of melanosome and the volume concentrationof melanosome in epidermis. Figure 2.10 shows the average absorption coefficient of51

melanosome as the function of wavelength [117]. For the human skin, typical ranges forthe volume concentration of melanosome Cmel in epidermis have been reported [118]: Cmel
= 1-3% for light-skinned Caucasian, 11-16% for Mediterranean, and 18-43% for darkly
pigmented African.
Hemoglobin exists in the erythrocyte and its absorption spectrum changes
according to the binding. Hemoglobin molecule bound to oxygen is called oxygenatedhemoglobin, while one bound to nothing is deoxygenated hemoglobin. Their spectra ofmolar extinction coefficients are shown in Fig.2.11. These spectral data are listed inAppendix A1.

Hematocrit (Hct) is expressed as the volume fraction or volumeconcentration of the red blood cells in the whole blood. Typical human whole blood
consists of 55% plasma and 45% red blood cells, and, thus, its hematocrit is Hct=45%.
In such a condition, hemoglobin has the concentration of 150g/liter of blood. The
absorption coefficient of whole blood having Hct=45% can be derived from the following
expression:a (cm-1 ) 

2.303   (cm-1  M -1 )  (150 g/liter).
64500 (g/mol)(2.65)

where 2.303 is the conversion constant from the common logarithm to natural logarithm,
and 64500 is the gram molecular weight of hemoglobin. The unit M is the molar
concentration, that is, (mol/liter). The absorption coefficient of dermis depends on boththe absorption coefficient of whole blood and its volume concentration Cb of in dermis.

52

(a) Scattering coefficient of epidermis and dermis.

Erythrocyte suspension
HCT= 45%(b) Scattering coefficient of blood.
Fig. 2.8. The published values of (a) scattering coefficient of epidermis and dermis,(b) scattering coefficient of blood, (c) anisotropy scattering parameter of
epidermis and dermis, and (d) anisotropy factor of blood.

53

gAnisotropy
factorparameter
scattering
Anisotropy
g [-]

1.00

0.95

0.90

0.85

0.80

0.75

0.70
400

450

500

550

600

650

700

750

800

850

900

Wavelengthλ(nm)Wavelength[-]parameter
scattering
Anisotropyg [-]gAnisotropy
factor(c) Anisotropy scattering parameter of epidermis and dermis.
1.00

0.95

0.90

0.85

0.80

0.75

0.70
400

450

500

550

600

650

700

750

800

850

900

Wavelength (nm)Wavelengthλ [-](d) Anisotropy scattering parameter of blood.
Fig. 2.8. The published values of (a) scattering coefficient of epidermis and dermis,(b) scattering coefficient of blood, (c) anisotropy scattering parameter of
epidermis and dermis, and (d) anisotropy factor of blood.

54

Fig. 2.9. The published values of molar extinction coefficient of melanin.

Fig. 2.10. The published values of absorption coefficient of single melanosome.

55

Fig. 2.11. The published values of molar extinction coefficient of oxygenatedhemoglobin and deoxygenated hemoglobin.

56

2.6.3 Refractive Index of Tissue
The refractive index n for the tissue has been studied theoretically andexperimentally. Takatani and Graham [119] have studied the values of refractive index
for the primary compounds which make up tissue theoretically and reported these values
range from 1.47 to 1.49. On the other hand, Jacques et. al, [92] calculated n = 1.37 at 
= 632.8 nm for tissue using a predictive method based on the water content of tissue.
Parrish et. al, [120] have reported a value of n = 1.55 for the stratum corneum. Bolin et.
al, [121] have investigated the refractive index for some mammalian tissues (bovine,
porcine, canine, and human) experimentally, and their results showed that the values of n
for most tissues range form 1.38-1.42 in the visible to near-infrared wavelength region.

2.7 Layered Skin Tissue Model
It is known that human skin has complex structure as shown schematically in Fig.
2.12(a). Studies of light propagation in skin tissue usually employ a two- or three-layered
model which consists of epidermis, dermis, and subcutaneous fat tissue for simplicity.
However, the three-layered model is insufficient for reliable investigation of various
changes in skin conditions. For example, melanin in epidermis and hemoglobin in dermis
have inhomogeneous distribution in a vertical direction. Regarding this aspect, Maeda et.
al. [122] developed a nine-layered skin tissue model on the basis of histologicalclassification. Figure 2.12 shows the nine-layered model of human skin tissue. There is
stratum corneum (layer L1) of about 10 µm in thickness in the top of skin tissue, andepidermis exists under stratum corneum.

Epidermal layer is composed of stratum

granulosum and stratum spinosum (layer L2), and stratum basale (layer L3). We consider

57

Thickness t [mm]

L1

L2
L3
L4

Thickness
Stratum corneum
0.015
Stratum granulosum,
0.060
Stratum spinosum
Epidermis
Stratum basale
0.015
Papillary dermis

L5 Subpapillary dermis

0.045
0.175

L6 Upper blood net dermis 0.080

L7

Reticular dermis

Dermis0.600

L8 Deep blood net dermis 0.100
L9(a) Cross section of skin(a) Cross section of skin

Subcutaneous tissue(b) Nine-layered skin model(b) Nine-layered model(Present)

6.000
[mm]

Subcutaneous
tissue(c) Three-layered skin model(c) Three-layered model(Previous)

Fig. 2.12 (a) Illustration of skin tissue having complex structure, (b) nine-layeredand (c) three-layered models.

58

that stratum granulosum and stratum spinosum have close histological nature, and treat
them as a single layer. The thickness of epidermis is about 100 µm or less usually. Dermis
extends under epidermis and is classified into five layers consisting of papillary dermis(layer L4), subpapillary dermis (layer L5), upper blood net dermis (layer L6), reticulardermis (layer L7), and deep blood net dermis (layer L8). Dermis is totally about 1.5 mm
thick. Subcutaneous fat tissue (layer L9) is several millimeters in thickness and extends
under dermis. Layers L1 to L9 are in parallel and we assume that scattering and absorption
properties are uniform within each of the nine layers.
If inhomogeneous distribution should be considered, layers may be increased
occasionally, to be specific by dividing any such layers into sublayers having different
absorption and scattering coefficients from one another. Inclusion of a specific blood
region such as an internal blood layer or a blood vessel may be possible as necessary.
Blood vessels in the skin tissue are mainly arteriole and venule in layers L6 and L8, andcapillary loop in layer L4. These blood vessels may contribute to absorption through thepresence of hemoglobin, as well as to scattering due to their network structure. So theeffect of these contributions may not be negligible, but it is difficult to treat the
distribution of vessels in this model. So, we assume that the overall effects of thesecontributions are captured as a part of values of absorption and scattering coefficients
through the proposed model, which reduces the complexity in calculation of lightpropagation. Moreover, a lot of fiber components such as collagen and elastin exist indermis and keep elasticity of the skin. It is known that both of these are also related toscattering properties in tissue, and are characterized by a scattering coefficient in thisscheme. On the other hand, pigments such as melanin in epidermis and hemoglobin indermis are closely related to absorption properties which depend on their concentration,

59

and are characterized by an absorption coefficient. In this way, the nine-layered structure
is considered to give more realistic skin tissue model than the conventional three-layeredtype used so far. Taking account of various conditions in scattering, absorption, andgeometry for each of the nine layers, one can settle various skin conditions. Thickness oflayers in Fig. 12 shows typical values obtained by modifying data of Meglinski andMatcher [123], which in this study referred to cheek or forearm area.

60

2.8 Spectral Reflectance Database
Spectral reflected database is constructed with various simulated reflectancecurves using a certain skin model. Through the process of spectral mining from thisdatabase one or more simulated spectra can be extracted for a measured spectrum using
a basic criterion of root mean square error. From the known optical parameter informationof the extracted spectrum, the unknown parameter information of measured spectrum isestimated. To construct the database with simulated spectra in a reasonable size, only
absorption coefficient µa and scattering coefficient µs are changed, keeping otherparameters refractive index n, anisotropy scattering parameter g and layer thickness t
fixed at the standard condition. Figure 2.13 represents some typical simulated spectra thatexists in the database. Furthermore, details of spectral database are discussed in chapters
4 and 5.

Fig. 2.13 Selected example of simulated spectra in the constructed database.

61

2.9 Agarose-gel Phantom
To confirm the feasibility of the proposed method, experiments on agarose-gel
phantoms has been carried out. To produce skin-alike phantoms a three-layered phantom
model is considered, in which layer Lp1, layer Lp2 and Lp3 represents epidermal layer,dermal layer and subcutaneous-fat tissue, respectively. The thickness of layers are: 0.6mm
for Lp1, 6.8mm for Lp2 and 4.65mm for Lp3. This set of selected thickness for layers
comes from the investigation of various other phantom model with different thickness,where the main prority was given to the nature of the phantom model in recreating thespecteal characteristics of human skin tissue. The current selected model with thickness
set mentioned above was by far the closest in generating spectral characteristics similar
to that of human skin.
In order to accumulate absorption effect or as an melanosome substitute, coffe
solution is used in layer Lp1. In case of layers Lp2 and Lp3, horse blood (Nihon Biotest
Kenkyujo, Saitama, Japan) with 44% of hematocrit is used as absorber. To achive thescattering effect in all the three layers of phantom different volume concentration (vol.
conc.) of 20% intralipos solution (Otsuka Pharmacutical Co. Ltd, Tokyo, Japan) areincorporated. Agar solution is used as the base material in each of the three layers. The
agar solution is prepared through the infusion of agar powdre (Ina Shokuhin Kogyo,
Nagano, Japan) in saline with a weight ration of 0.8%. No deoxygenetaing agent (forexample Na2S2O4) is added in layers Lp2 and Lp3, as most of the time it produces chemical
imbalance and influences change in optical properties of these layers. So, it is assumed
that the oxygeneation level of layers Lp2 and Lp3 is of 100%. Figure 2.14 shows anexample of stacked three-layered agarose-gel phantom.

62

Layer Lp 1

Layer Lp 2

After stacking of all the layers
Layer Lp 3

Fig. 2.14 Example of an agarose-gel phantom used in this study.

63

Chapter 3
Effects of Detection Area on Measurements of Spectral
Reflectance in Human Skin Tissue

3.1 Introduction
Light propagation in biological media is interesting physical phenomenon. Humanbody is especially an important medium in which light is absorbed and scattered in the
wavelength-dependent manner. In a visible (VIS) to near-infrared (NIR) range of
wavelength, light is known to penetrate in the human tissue in depth of 1~3 mm [124].
This region corresponds successfully to human skin tissue. From this nature, lightpropagation in human skin tissue have received much attention from researchers who
study and develop techniques for measuring concentrations of chromophores such as
melanin and hemoglobin [74,125], tissue structural abnormality [126,76,127,128], andskin color [128]. These give useful information for dermatology and cosmetic industry.
In general, propagation of light in VIS-NIR wavelength is investigated byspectrophotometry which gives spectral reflectance. By this typical instrumental
approach, many researchers studied the method for estimating melanin and hemoglobin
concentration and blood oxygenation dynamics [74,125,128] from analysis of spectral
curves. Various types of spectrophotometric systems are commercially available and caneasily be used for spectral reflectance measurements.
It should be noticed, however, that measured spectral reflectance contains
contribution of light component coming back from a diffusing object and condition of
measurements depends on property of light diffusion inside the object. Most direct64

measuring condition relating to the light diffusion is a size of a detecting aperture andsecondary an area of illumination. When the skin spectral reflectance is measured, thesize of a detecting aperture should appropriately be set by considering diffusion propertiesof light in skin tissue. However, general-purpose spectrophotometer contains only two or
three preset sizes of the detecting aperture. Therefore, the necessary size should be
investigated for appropriate use of the spectrophotometer which is specific to human skintissue.
In this study, we investigate the effect of a detecting aperture size on
measurements of spectral reflectance in human skin tissue by numerical simulation
followed by experiment. For this, we employed Monte Carlo simulation of lightpropagation in a layered skin tissue model [11], and discussed results of simulated spectralreflectance and photon fluence map. A result of experiment with an integrating spheredemonstrates the same tendency with that of the simulation, though values of reflectancechanges are different between simulation and experiment.

3.2 Model of Light Propagation in Skin Tissue

3.2.1 Skin Tissue Model
As introduced in chapter 2, skin tissue has complex structure which isinhomogeneous and shaped irregularly, and has anisotropic physical properties. From ahistological point of view, skin tissue is often considered by multi-layered structure which
is useful for numerical modelling. Though the conventional studies generally employ a65

three-layered model consisting of epidermis, dermis, and subcutaneous fat tissue asshown in Fig. 2.12(c), Maeda et. al. developed a nine-layered skin tissue model [122] asshown in Fig. 2.12(b) on the basis of detailed histological knowledge. In Fig. 2.12, typical
thickness of each layer is given by literatures [7,122,123,127,129]. Chromophores suchas melanin in epidermis and oxygenated or deoxygenated hemoglobin in dermis andsubcutaneous tissue are main factors of light absorption properties which depend on theirconcentration. Cells and fiber components such as blood cells, collagen, and elastin areclosely related to light scattering properties due to difference in refractive index. In thismodel, we assume that absorption and scattering properties are uniform in each of thenine layers. The nine-layered skin tissue model is successfully used for Monte Carlosimulation of light propagation. By setting of values for necessary optical and geometricalparameters in each layer, realistic spectral reflectance curves and visual photon fluence
distributions are obtained.

3.2.2 Monte Carlo Simulation
Figure 3.1 shows a concept of Monte Carlo simulation (MCS) for lightpropagation in multi-layered tissue [11]. The MCS treats light as a bundle of photons. A
photon of unit weight enters at the origin of x, y, and z axes on the tissue surface andbegins propagation. Random numbers generated by a computer give a propagating length
S and direction (θ and ψ) step by step, which are characterized by five input parametersspecified to each layer at each wavelength: scattering coefficient µs, absorption coefficientµa, anisotropy scattering parameter g, refractive index n, and thickness t. Typical valuesof the parameters can be given by those in literatures [7,122,123,127,129].

66

Incident photon :photon weight, w1 = 1
Number of photons: 105-107

yReflectance

x

ms
w2 =
w
ms + ma 1tParameters for calculation

ms : Scattering coefficient
ma : Absorption coefficientw3S

ms i-1wi =w
ms + ma i-1(x’, y’, z’)qyg : Anisotropy parametern : Refractive index
t : Geometrical thickness

w4

D wj, j-1 =zTransmittance(x, y, z)ma
ms + ma

Absorption

S : Step sizeq : Deflection angle
y : Azimuthal angle

Fig. 3.1 Concept of Monte Carlo simulation of light propagationin multi-layered tissue.

A traveling length in which the photon undergoes neither an absorbing event nor
a scattering event is considered as a free path length. After traveling the path length of
one step, the photon weight is attenuated at a rate determined by absorption and scattering
coefficients. This step-like propagation is repeated by continuous generation of random
numbers. When traveling on many steps and the resultant weight is smaller than a preset
threshold (10-6 in this study), then this photon is regarded as negligibly attenuated, andthe second photon enters the tissue. By accumulating values of the weight that are reemitted from the top surface, reflectance is calculated with reference to the total values

67

of incident photon weight. We used 106 photons to obtain a stable reflectance result at
each wavelength. This process is carried out at each of the necessary wavelength points,
and finally spectral reflectance is obtained. We took the process in the wavelength range
380-780 nm at an interval of 10 nm.
During the MCS process mentioned above, the computer records and stores all
the values of the photon weight with its position in the three-dimensional manner. Thus,
the MCS algorithm [11] used in this study provides the amount of light absorption in eachvolume element of the skin tissue model. This amount is referred to as photon fluence ϕ[11] given by [ix , i y , iz ] A [ix , i y , iz ]ma

[cm-2](3.1)

where A denotes the amount of light absorption per unit volume, and ix, iy, and iz indicate
the element number of each grid in the horizontal (ix, iy) and vertical (iz) directions in the
numerical skin tissue model. Here, we investigate the fluence in x-z plane with y = 0 in
this paper.
We have two different modes in the calculation of photon fluence. One is the total
fluence which include all the reflected, absorbed, and transmitted components of light,
and another is the detected fluence which contains only reflected component returning to
the skin surface. The latter fluence presents light component which is measurable
instrumentally on the skin surface. Both these two can visually be expressed byillustration as energy distribution pattern of light propagation.

683.3 Numerical Investigation

3.3.1 Method and Condition
A surface of a skin tissue model is designated by x and y axes, and a direction ofpenetration into the deep layers is given by z axis. As shown in Fig. 3.2, photons ofincident light are launched one by one into the tissue perpendicularly. We investigatedthree different areas of illumination by a point, circular-spot with 2 and 4 mm in diameter
di. As for the detection area, we investigated various diameters d of 0.2-40 mm, and all
the direction of photons re-emitted from the surface into space are accounted as it is withan integrating sphere. No polarization is considered, but Fresnel reflection law is includedin the algorithm. In this study, we assume human forearm skin as an object to be measured
and employed its typical values of the MCS parameters from the literature[7,122,123,127,129].

Detection aread
Incident
point

x
y

z
di

Incident area
Fig. 3.2 Definition of a point and area for illuminationand detection on the skin surface.
693.3.2 Point-like Illumination
Figure 3.3 shows simulated spectral reflectance curves with various diameters ofthe detection area in the case of point illumination. Results with the large detection area
having d = 6 - 40 mm in diameter demonstrate no significant difference in the curves
which are overlapped. With reducing the diameter d, reflectance is decreased especiallyin the longer wavelength range. Figure 3.4 demonstrates the photon fluence maps in x-z
plane for the case of 550nm wavelength. The map in (a) of Fig. 3.4 gives the total fluence
distribution, while that in (b), (c), and (d) gives the detected fluence distributions. The
latter maps are found to depend on diameter d of the detection area. Figure 3.5
demonstrates the photon fluence maps for a case of 700 nm wavelength in the same way
as Fig. 3.4. It is clear that light penetrates into the deeper and is diffused more widely.
This property indicates that light component penetrated into the deeper layers anddiffused and coming back to the surface is not sufficiently detected in cases of using small
diameters of the detecting aperture. This tendency is significant in the longer wavelength
range in which light penetrates into the deeper and broadens widely in the skin tissue. It
is found that the smaller area of detection is unacceptable for appropriate spectralreflectance measurements. The results of Figs.4 to 6 are specific to an ideal situation of
the point-like illumination. More realistic situation is to use a circular-spot illumination.

70

d=40mm
20mm
10mm
6mm
4mm
2mm
1mm
0.6mm
0.4mm
0.2mm

Point

d=6~40mm

d=2mm

d=0.2mm

Wavelength [nm]Fig. 3.3 Simulated spectral reflectance curves with different diameters d ofa detecting aperture for a point-like illumination.

71

d

104
4

0

5

4.5

102
2

0.8

4

1

z [mm]

0.7
0.6
0.5

3.5

1
0

3

2

2.5

(a) Total photons

0.4

10-2
-2

(b) d = 1mm

2

0.3

1.5

3

0.2

10-4
-4

1

0.1

0.5

0
0

0.1

4

0.2

0.3

-2

0.4

-1

0.5

0.6

0.7

0
x [mm](c) d = 6mm

1

0.8

0.9

2

1

(d) d = 10mm

Fig. 3.4 Two-dimensional distribution maps of (a) the total photon fluenceand (b) – (d) the detected photon fluence with d = 1, 6, and 10mm
in diameter of the aperture, respectively, in 550 nm wavelength ofa point-like illumination.

72

-4

10-6
10
-6

0

Photon fluence [cm-2]

1
0.9

d

104
4

0

5

4.5

102
2

0.8

4

1

0.6
0.5

z [mm]

0.7

3.5

1
0

3

2

2.5

(a) Total photons

0.4

10-2
-2

(b) d = 1mm

2

0.3

1.5

3

0.2

10-4
-4

1

0.1
0
0

0.5
0.1

4

0.2

0.3

-2

0.4

-1

0.5

0.6

0.7

0
x [mm](c) d = 6mm

1

0.8

0.9

2

(d) d = 10mm

Fig. 3.5 Two-dimensional distribution maps of (a) the total photon
fluence and (b) – (d) the detected photon fluence with
d =1, 6, and 10mm in diameter of the aperture, respectively,
in 700 nm wavelength of a point-like illumination.

73

1

-4

10-6
10
-6

0

Photon fluence [cm-2]

1
0.9

3.3.3 Circular-spot Illumination
Figures 3.6 and 3.7 show simulated spectral reflectance curves with various
diameters d of the detection area in cases of circular-spot illumination having di = 2 and
4 mm in diameter, respectively. In Fig. 3.6, reflectance curves for d ≤ 4 mm significantly
than those in Fig. 3.3. This is probably due to insufficient detection area with respect tothe diffused light component which is produced by di = 2 mm diameter illumination. In
Fig. 3.7, reflectance curves for d ≤ 6 mm are clearly reduced in the same way as those ford ≤ 4 mm in Fig. 3.6. This is also because of insufficient diameter for the detection withrespect to di = 4 mm diameter illumination.

d=40mm
20mm
10mm
6mm
4mm
2mm
1mm
0.6mm
0.4mm
0.2mm

di=2mmd=6~40mm

d=2mm

d=0.2mm

Wavelength [nm]Fig. 3.6 Simulated spectral reflectance curves with different
diameters d of a detecting aperture for a circular spot
illumination of di = 2 mm in diameter.

74d=40mm
20mm
10mm
6mm
4mm
2mm
1mm
0.6mm
0.4mm
0.2mm

di=4mmd=10~40mm

d=4mm

d=2mm

Wavelength [nm]Fig. 3.7 Simulated spectral reflectance curves with different
diameters d of a detecting aperture for a circular spot
illumination of di = 4 mm in diameter.

75

Figures 3.8 and 3.9 give the photon fluence maps in 550 nm and 700 nm
wavelength cases for di = 2 mm circular spot illumination. The maps in (a) of Figs. 3.8
and 3.9 show the total fluence while those in (b) to (d) show the detected fluence. By an
areal illumination with di = 2 mm in diameter, the photon fluence demonstrates
horizontally broaden distribution in comparison with those of Figs. 3.4 and 3.5. The
fluence maps in 700 nm are again found to be more widely diffused than those in 550 nm.

ddi=2mm

104
4

0

5

4.5

102
2

0.8

4

1

0.6
0.5

z [mm]

0.7

3.5

1
0

3

2

2.5

(a) Total photons

0.4

10-2
-2

(b) d = 1mm

2

0.3

1.5

3

0.2

10-4
-4

1

0.1
0
0

0.5
0.1

4

0.2

0.3

-2

0.4

-1

0.5

0.6

0.7

0
x [mm](c) d = 6mm

1

0.8

0.9

2

(d) d = 10mm

Fig. 3.8 Two-dimensional distribution maps of (a) the total photon
fluence and (b) – (d) the detected photon fluence withd = 1, 6, and 10mm in diameter of the aperture, respectively,in 550 nm wavelength of a circular spot illumination of
di = 2 mm in diameter.

76

1

-4

10-6
10
-6

0

Photon fluence [cm-2]

1
0.9

d
di=2mm

104
4

0

5

4.5

102
2

0.8

4

1

0.6
0.5

z [mm]

0.7

3.5

1
0

3

2

2.5

(a) Total photons

0.4

10-2
-2

(b) d = 1mm

2

0.3

1.5

3

0.2

10-4
-4

1

0.1
0
0

0.5
0.1

4

0.2

0.3

-2

0.4

-1

0.5

0.6

0.7

0
x [mm](c) d = 6mm

1

0.8

0.9

2

1

Photon fluence [cm-2]

1
0.9

-4

10-6
10
-6

0

(d) d = 10mm

Fig. 3.9 Two-dimensional distribution maps of (a) the total photon
fluence and (b) – (d) the detected photon fluence withd = 1, 6, and 10mm in diameter of the aperture, respectively,
in 700 nm wavelength of a circular spot illumination of
di = 2 mm in diameter.

Figures 3.10 and 3.11 show the maps in 550 and 700 nm wavelength cases for di= 4 mm circular spot illumination. An increase of diameter from 2 to 4 mm results infurther broadening of the photon fluence distribution in a horizontal direction. The
diameter d of 6 mm or more in the detection area is probably necessary for receiving
substantial amount of the diffused light component coming back to the surface.

77d

di=4mm

104
4

0

5

4.5

102
2

0.8

4

1

0.6
0.5

z [mm]

0.7

3.5

1
0

3

2

2.5

(a) Total photons

0.4

10-2
-2

(b) d = 1mm

2

0.3

1.5

3

0.2

10-4
-4

1

0.1
0
0

0.5
0.1

4

0.2

0.3

-2

0.4

-1

0.5

0.6

0.7

0
x [mm](c) d = 6mm

1

0.8

0.9

2

(d) d = 10mm

Fig. 3.10 Two-dimensional distribution maps of (a) the total photon
fluence and (b) – (d) the detected photon fluence withd = 1, 6, and 10mm in diameter of the aperture, respectively,in 550 nm wavelength of a circular spot illumination of
di = 4 mm in diameter.

78

1

-4

10-6
10
-6

0

Photon fluence [cm-2]

1
0.9

d
di=4mm

104
4

0

5

4.5

102
2

0.8

4

1

0.6
0.5

z [mm]

0.7

3.5

1
0

3

2

2.5

(a) Total photons

0.4

10-2
-2

(b) d = 1mm

2

0.3

1.5

3

0.2

10-4
-4

1

0.1
0
0

0.5
0.1

4

0.2

0.3

-2

0.4

-1

0.5

0.6

0.7

0
x [mm](c) d = 6mm

1

0.8

0.9

2

(d) d = 10mm

Fig. 3.11 Two-dimensional distribution maps of (a) the total photon
fluence and (b) – (d) the detected photon fluence with
d =1, 6, and 10mm in diameter of the aperture, respectively,
in 700 nm wavelength of a circular spot illumination of
di = 4 mm in diameter.

79

1

-4

10-6
10
-6

0

Photon fluence [cm-2]

1
0.9

3.4 Experimental Investigation

3.4.1 Method and Apparatus
Figure 3.12 shows a measuring apparatus for the spectral reflectance using an
integrating sphere and spectrophotometer. Light from a halogen lamp source (Hayashi
LA-150UX) was focused by lens to skin surface with a circular spot of di = 4 mm indiameter via a sample window of a 6-inch integrating sphere (Labsphere RT-060-SF).
Diffusely reflected light from the skin was received at the detection port by a fiber probe
having 400 µm diameter. The detected area was changed by setting detecting aperture
having diameters d = 4, 5, 6, 8, 10, 15, and 22 mm on the sample window. The received
light was transmitted into a multichannel spectrophotometer (Ocean optics USB-4000),
which measures spectral reflectance in a visible wavelength range of 380-780 nm.
White light(Halogen lamp)

Human skin

Integrating sphereLight trap

Illuminated area
di = 4 mm

Lens
Detector port
Optical fiber

Detection aread = 4 ～22 mm

100

Reflectance R [%]

80
60
40
20
0

400

500

600

700

wavelength [nm]Spectrophotometer

Sample window

PC

Fig. 3.12 Measuring apparatus for spectral reflectance using a
6-inch integrating sphere.
803.4.2 Results
Figure 3.13 shows spectral reflectance curves measured by different diameters dof the detecting aperture on the human forearm skin. Measured reflectance decreases as
the detecting aperture becomes small. This decrease is significant in the wavelength range
longer than 600 nm. The curves with d = 10, 15, and 22 mm seem to have almost the same
behavior. The absolute values in reflectance are different between simulated curves in Fig.
3.7 and measured ones in Fig. 3.13 for the same detecting aperture diameter. However,the simulation and measurement demonstrate the same reduction behavior in reflectanceespecially in the longer wavelength range with insufficient size of the detection area. It
can be seen that the appropriate diameter of a detecting aperture is estimated to be 10 mm
or more from both the simulated and measured results in case of di = 4 mm spot
illumination. This estimation is of course dependent on the size of illuminating spot.
However, a necessary margin of the detection area is supposed to be about 4 to 6 mm byconsidering the broadening in the diffused light in skin tissue. Therefore, the appropriate
diameter for detection area is estimated to be the sum of illuminating spot diameter andthis margin of 4 to 6 mm for human skin.

81d=22mm di=4mm
15mm
10mm
8mm
6mm
5mm
4mm

d=15mmd=20mmd=4mm

Fig. 3.13 Measured spectral reflectance curves with different
diameter d of a detecting aperture for a circular spot
illumination of di = 4 mm in diameter on a humanforearm skin.

82

3.5 Conclusion
This chapter investigated effects of the detecting aperture size on spectralreflectance curves in human skin tissue by Monte Carlo simulation and simple
spectrophotometric experiments on human forearm. Undesirably small size of detecting
aperture causes reduction of measured reflectance especially in the longer wavelength
range. The necessary size is probably given by the sum of an illuminating spot size and a
detection margin of 4 to 6 mm which results from the broadening of diffused light in theskin tissue. This estimation is useful for reliable measurement of spectral reflectance inskin tissue in the fields of dermatology and cosmetic product development. The study
presented in this chapter is specific to human skin and may have some different properties
for different objects to be measured. However, the same kind of investigation anddiscussion may be effective for the other objects.

83Chapter 4
Simulated Reflectance Spectra and Point Spread Functions
in Database Constructed by Moderate Grouping ofNine Layers in Skin Model

4.1 Introduction
Over the years, multiple studies [49,51,52,55,125,130] have been carried out on
how the appearance of human skin reflects physiological condition in the tissue, using
optics as an effective tool. Generally, the skin tissue is analyzed by measuring spectralreflectance in a visible wavelength range. Analysis of spectral reflectance provides avariety of knowledge about skin condition such as skin color, skin diseases and skin aging.
The spectral reflectance is governed mainly by absorption and scattering coefficients inthe skin tissue which are very closely related to the concentration of typical chromophores
as melanin and hemoglobin and the tissue morphology which may alter under various
skin diseases and conditions [64-67]. In this respect, the skin spectral reflectance is adefinitely useful measure for diagnostics of skin diseases in dermatology and monitoringof the skin color and tissue conditions in the cosmetic field. It is, however, generally
difficult to analyze directly skin tissue conditions including pigment concentration andtissue structural abnormality from measured spectral reflectance. Thus, several different
methods have been studied on a relation between the skin condition and spectralreflectance [131-132].
Shimada et al. [57-59] presented studies on the multiple regression analysis forassessment of melanin and blood concentration in human skin. This type of linear84regression method became popular and further studied by some groups [73-76] for
estimation of skin conditions. Tsumura et al. [56] studied independent-component
analysis of skin color image for evaluating melanin and hemoglobin distribution onhuman face. The regression methods are influenced by ranges of parameters used toderive the regression equation. In contrast to these approaches, Zhang et al. [68,69]
presented the method using the diffusion approximation, and Magnain et al. [70,71]
studied the solution of the radiative transfer equation by use of the auxiliary function
method for determination of skin optical properties and skin color. These approaches aretheoretically clarified, but have some limit in application. Ohtsuki et al. [133] reported
application of the Kubelka-Munk theory for estimation of pigment concentrations. Thismethod has difficulty in use for an inhomogeneous skin tissue model.
The above-mentioned various approaches have the common drawback ofdifficulty in application to various skin conditions including normal to abnormal. To
overcome the difficulty, a group of Zhang et al. [68,69] developed their idea to library
based spectral fitting for determination of optical properties in their framework of the
diffusion model. They showed reasonable estimation of nine skin parameters by genetic
algorithms, but their skin model was limited to the layers of epidermis and dermis.
Magnain et al. [70,71] tried to develop their skin model from 5 to 22 layers in theirtreatment of the radiative transfer equation with the auxiliary function method. They
discussed the shape of reflectance spectra in detail with relation to skin parameters. In
their model, however, the optical parameters are not discriminated layer by layer. Thus,
the effect of absorption and scattering in each layers are only qualitatively discussed on
the total spectral shape.
Changes of skin condition from normal to abnormal are primarily estimated by85

absorption and scattering coefficients in skin tissue. The absorption coefficient is defined
as a product of molar extinction coefficient and molar concentration of chromophore suchas melanin in epidermis and oxygenated and deoxygenated hemoglobin in dermis. The
scattering coefficient is closely related to tissue structure having various cells since lightscattering occurs at the boundary between different refractive-index media. It is, thus,
desirable that a method for analysis of spectral reflectance guarantees a scalable range ofvalues estimated for the absorption and scattering coefficients. To this aim, the Monte
Carlo method is advantageous for numerical simulation of light propagation in skin tissue[11]. Meglinski and Matcher [123,129] presented simulation of reflectance spectra using
a seven-layered skin model. Their approach is useful for analyzing elemental factors ofabsorption and scattering layer by layer, but they assumed wavelength-independency of
scattering condition. To make more realistic simulation, Maeda et al. [122] developed thenine-layered model with the set of five parameters (absorption coefficient, scattering
coefficient, anisotropy scattering parameter, refractive index, and thickness) layer bylayer. By adjusting a value of each parameter, they demonstrated well simulated spectra
similar to the measured spectra. We use this approach to estimate the absorption andscattering coefficients layer by layer, since we consider that the five parameters used for
producing the well-simulated spectrum give reasonable candidate values for the measured
spectrum. However, a manual iteration is quite time-consuming and unstable process forfinding a simulated spectrum well similar to the measured spectrum.
By referring to the idea used in the work of Zhang [69], in this study, a skinreflectance spectral database has been constructed in the framework of the nine-layered
skin tissue model which is used for determining the absorption and scattering coefficientsof a measured spectrum by spectral mining in the database. To build a reasonable size of86

database, we propose a moderate grouping method [134] for nine layers by using two
magnification factors to the absorption and scattering coefficients. This database suffersfrom uncertainty or ambiguity in identification of a simulated spectrum which seems
close to a measured spectrum since two or more different combinations of absorption andscattering coefficients produce similar reflectance spectra [135] in simulation. To resolve
this situation, we also discuss in this paper possibility of using a point spread function(PSF) of reflected intensity on the skin surface, which strongly exhibits scattering
property of skin tissue rather than the absorption.

4.2 Simulation Model for Spectral Reflectance

4.2.1 Skin Tissue Model
Human skin tissue has a very complex structure with different optical properties
to contribute to its light propagation nature. Previously it was known that human skintissue was modeled of three layers such as epidermis, dermis and subcutaneous layers, as
can be seen in Fig. 2.12(c). To imitate human skin condition with better accuracy, a ninelayer model [122] has been taken into account as shown in Fig. 2.12(b) and each layer
has its own separate thickness and optical properties. This nine-parallel layered model
was used for performing simulation of spectral reflectance and PSF in this study. Moredetailed explanation regarding nine-layered skin model has been described in Section 2.7
of Chapter 2, including information about all the necessary optical properties.

874.2.2 Monte Carlo Method for Simulation of ReflectanceIn the Monte Carlo simulation (MCS) of light propagation in tissue media[10,136-140], light is treated as a bundle of photons. Pseudo random numbers generated
by a computer statistically determine trajectories of photons in the media, which arecharacterized by optical properties such as absorption coefficient and scattering
coefficient. A conceptual diagram of the MCS method for light propagation in multilayered tissue is described in Fig. 3.1 [11]. A photon bundle of unit energy (W1=1) enters
the tissue and undergoes scattering and absorption. A travel distance in which the photon
undergoes neither an absorbing event nor a scattering event is defined as the free path
length and denoted by a step size s. This length s is given by – ln () / (µs + µa) with arandom number  ranging from 0 to 1 with scattering coefficient µs and absorption
coefficient µa. After traveling the path length, the photon energy is assumed to beattenuated at a rate of µs / (µs + µa) determined by scattering and absorption coefficients.
If the resultant energy is smaller than a preset threshold (10-6 used in this study), then thisphoton bundle is regarded as negligibly attenuated, and the second photon bundle enters
the tissue. When the energy is still larger than the threshold, propagation continues andnew traveling direction (θ, ψ) is given by random numbers, where θ is the deflection orpolar angle and ψ is the azimuthal angle. By accumulating the energies of photons thatare emitted from the top surface to the detection side, reflectance is calculated withreference to the total energy of incident photons. This process is performed at each of thenecessary points of wavelength λ, and thus spectral reflectance is obtained numerically.
In this study, the above process is performed in the wavelength range of 400700nm at an interval of 10 nm. To obtain stable simulation results, 105-107 photons are

88usually necessary from our experience. For the MCS, we used the algorithm of Wang etal. [11], in which the five input parameters must be specified at each wavelength for eachlayer: scattering coefficient µs, absorption coefficient µa, anisotropy scattering parameterg, refractive index n, and thickness t. Furthermore, oxygen saturation Sat-O2 should also
be specified. g is the average cosine value of the phase function P(θ), and it ranges from
−1 to +1, corresponding to the perfect backscattering and forward scattering. By
following the algorithm of Wang et al., we employ the Henyey-Greenstein function [110]as a well-known approximated form of the phase function, thus, g is characterized by thisfunction. As is known usually, typical resultant values of g are 0.7-0.8 for tissue andgreater than 0.9 for blood. Each value of g for each layer is set by published data [123,141144] and our experience, which is shown later in this chapter. The refractive index n is awavelength-dependent function but is assumed to be constant in the wavelength range
used, while set different layer by layer. For specifying values of these input parameters,
we surveyed several articles having some plausible values at specific wavelength points[76,123,127,141-146] and, then modified them in experiments and simulations so thatsome simulated spectra could match measured sample spectra [122]. We employ thesevalues as a standard condition, as can be seen in Section 4.3. Then, one of the parameters
in a specified layer was varied from the standard value and the others were fixed to be thestandard, and MCS was carried out to produce many spectral reflectance curves forvarious cases.

894.3 Simulated Reflectance Spectra and PSF in a Standard Condition
Figure 4.1 depicts a typical example of simulated reflectance spectrum and point
spread functions in three different wavelengths in the standard condition. Table 4.1
represents example of the input data set (5 parameters in each of nine layers) in a specificwavelength (700nm) used as the standard condition. It is noticed that four opticalparameters except for thickness have wavelength dependency nature. Their spectral
properties are shown in our preceding work [122]. In Fig. 4.1(a), the spectral reflectanceis low in the shorter wavelength range and large in the longer wavelength range while
showing a well-known ‘W’-like shape coming from the absorption characteristics ofoxygenated hemoglobin [147]. These features of the spectrum resemble to typicalappearance of a human skin spectrum. For the PSFs in Fig. 4.1(b), the vertical axis isnormalized by the maximum intensity of each PSF curve and also its intensity decay ispresented in a range of 0-1mm in x axis, in which x=0 means the center point of anilluminating spot having a diameter of 0.8 mm. As it is known that the PSF has
wavelength dependency due to the nature of optical path length in tissue. With the longer
wavelength, the PSF curve becomes broadened and is desirable for estimating change ofskin condition, which is described later. By this reason, the wavelength of 700nm is
chosen for the investigation on simulated PSF conducted in this study.

90

Table 4.1 Input data-set for nine layers at 700nm in the standard condition.

Layer

Refractive
Index
(n)

Absorption
coefficient(µa) [cm-1]

Scattering
coefficient(µs) [cm-1]

Anisotropy
factor(g)

Thickness(t) [cm]

L1

1.5

0.27

108.100.88

0.0015

L2

1.34

2.35

81.10

0.82

0.006

L3

1.34

21.1

432.300.82

0.0015

L4

1.4

0.37

1426.600.92

0.0045

L5

1.4

0.30

2118.200.92

0.0175

L6

1.39

0.41

2593.74

0.97

0.008

L7

1.4

0.29

2377.60

0.82

0.06

L8

1.38

0.37

2550.51

0.97

0.01

L9

1.44

0.32

2191.43

0.77

0.6

Intensity [-]Reflectance [%]

500nm
600nm
700nm

Distance x [mm](b) PSF

Wavelength [nm](a) Spectrum

Fig. 4.1 Typical spectrum and PSFs in different wavelengths
simulated with standard values in parameters.

91

4.4

Construction of Database

4.4.1 Spectral Fitting
It is known that a measured reflectance spectrum is used to derive the content ofmelanin and hemoglobin in skin tissue by using, for example, multi-variate analysis.
However, preparation of regression matrix is usually a difficult task for general purposes.
We studied the MCS-based spectral fitting method [148] in which a simulated spectrum
can be produced so that it agrees well with the measured spectrum by means of manual
iteration in the MCS. Finally-accepted spectrum is referenced to provide its absorption
and scattering coefficients, a and s in each of nine layers. This approach provides
flexible fitting potentiality, but it is difficult to adjust appropriately values of fiveparameters in each of the nine layers. Knowledge obtained from the simulated spectral
library [149] may probably be helpful for decreasing the iteration cost, but it requires
sufficient experience. So, the fitting is yet time-consuming and less reproducible.

4.4.2 Spectral Database
To improve the process of identifying the spectrum agreeing well to a measured
spectrum, we employ a spectral database method in which the most plausible spectrum is
extracted from the database with a criterion of root mean square error (RMSE), which isdefined here by

92

𝑚

1
𝑅𝑀𝑆𝐸 = √ ∑{𝑅𝑠 (𝜆𝑖 ) − 𝑅𝑡 (𝜆𝑖 )}2
𝑚

,

(4.1)

𝑖=1

where Rs(i) and Rt(i) are simulated and target (measured) spectra, respectively, and i
indicates an i-th discrete value of wavelength and m is the total number of discrete
wavelength points contained in the spectral data. We recognize a condition of RMSE ≤1% as acceptable agreement criterion in the beginning for all the target spectra, since thiscriterion extracts simulated spectra very close to the target spectrum in our preceding
works [134,135]. With this criterion, we consider that one or more simulated spectra canbe extracted from the database for the measured spectra in general. However, it may also
be the case with no extraction for RMSE ≤ 1%. For this case, we consider two optional
approaches. One is to increase the number of data by enlarging the range of opticalparameters in simulation. Another is to loosen the condition of RMSE, for example ≤ 2 ~3%, though the estimation accuracy may be degraded.
To construct the database, we intended to prepare a great number of spectral data
by using various values uniformly for all the five parameters (a, s, g, n, and t) in eachof the nine layers. However, this is unrealistic since the database may potentially containa number of useless spectral data, which correspond to unreal skin conditions, and cause
unnecessary effort for spectral matching. Then, we carefully considered the behaviors ofreflectance spectra in the above-mentioned library [149]. Among the five parameters, theabsorption coefficient a and scattering coefficient s are considered as the primaryfactors to find changes in the skin condition. The rest three parameters g, n, and t are also
related to some changes in skin, but we consider that probably they do not yield93

substantial changes in their values that can be found from a spectral change. Therefore,
we assume that anisotropic scattering parameter g, refractive index n, and layer thickness
t take constant values with keeping different values layer by layer, and we change valuesof the absorption and scattering coefficients, a and s in the nine layers. By thisassumption, the number of parameter values to be changed is reduced from 45 (5parameters by 9 layers) to 18 (2 by 9). In the constructed database, each spectrum iscontained with its associative data-set of 45 parameter values. Once a simulated spectrum
matched to the measured spectrum is found in the database, we can obtain 45 values forthe five parameters in the nine layers. To construct a reasonable size and easy-to–use
database with keeping this unique advantage of the nine-layered skin model, we propose
a moderate grouping method [134] for the nine layers by using two magnification factors
Ma and Ms for the absorption coefficient a and scattering coefficients, respectively.
Change of values in a and s is performed by Ma and Ms in terms of a multiplied by Maand s multiplied by Ms, respectively. It is known that the absorption coefficient a ischaracterized by melanin in epidermis and hemoglobin in dermis and subcutaneous layer.
To change a value of a in a convenient way, we set two groups of layers, L1 – L3 and L4– L9 and change the value of the absorption coefficient a by magnification factors,
specifically denoted in Ma,1-3 and Ma,4-9, respectively. It is supposed that the scattering
coefficient s is closely related to tissue structure and is characterized by three regions of
epidermis, the upper dermis, and the lower dermis followed by subcutaneous fat layer.
For the scattering coefficient s, we set three groups of layers, L1 – L3, L4 – L6, and L7 –L9 and change the value by magnification factors, specifically denoted in Ms,1-3, Ms,4-6,
and Ms,7-9, respectively. Each of these takes a range for varying values as given in Table

94

4.2.

Table 4.2 Magnification values used for producing
database. IL is intervals where L indicatesa grouping range of layers.

Layers

Ma

Ms

1~3

0.6 ~ 1.6
(I1-3= 0.10 )0.6 ~ 1.4
(I1-3= 0.10 )4~6

0.2 ~ 3.6
(I4-9= 0.20 )0.3 ~ 9.3
(I4-6= 0.30 )
0.1 ~ 1.0
(I7-9= 0.10 )7~9

It should be noted that, this grouping of a (in 2 groups) and s (in 3 groups) makes
the magnification in each group uniform but keeps the condition of different values fora and s in all the nine layers. To determine the number of the discrete steps, first weevaluated the range of change for each magnification factor so that a difference range
between the minimum and maximum reflectance spectra could cover all the target spectraused in this study. Then we set the smallest discrete step available (0.1 in this study) for
each magnification factor. However, the ranges of Ma,4-9 and Ms,4-6 are larger than thoseof the other magnification factors, and it results in the larger number of steps, which
increases the computation cost and the database size. So, we optimized and finally95employed the step to be 0.2 for Ma,4-9 and 0.3 for Ms,4-6. In this study, the number of
discrete steps for changing is 11 for Ma,1-3, 18 for Ma,4-9, 9 for Ms,1-3, 31 for Ms,4-6, and 10
for Ms,7-9, and therefore, the database accumulates totally 552,420 spectral curves. It isalso noted that a ratio of a- or s-values in layers within each group is maintained when
this group-based changing of parameters are performed. So, the advantage of having ninelayers as a skin model is guaranteed in this database. As mentioned earlier, the wavelength
for all spectral reflectance curves is in a range of 400-700nm at an interval of 10 nm (31wavelength points) to make the size of database reasonable. Figure 4.2 shows someexamples of simulated spectra included in the constructed database. These examples wereselected to represent in the figure a variety of simulated spectra contained in the proposed
database, and have a difference range about 10-15% in reflectance over the wavelengthof 400nm-700nm. As a special mention, the thickness t is not only different in individual
but also has variance in different positions of body. So, it is difficult to incorporate suchvariance in the present constructed database. Estimation of a and s values are madeunder the condition of fixed thickness values, since this version of database treats only
the change in a and s values but keeps fixed values for g, n, and t, which may be related
to a standard virtual person. At least, with this present version we can observe relativechange in a and s values in each of different subjects. In the near future, we intend todevelop an advanced version of database, where change in thickness values will beincorporated.

96

Reflectance [%]Wavelength [nm]Fig. 4.2 Example of spectral reflectance in database.

4.4.3 Numerical Validation ExperimentTo estimate the performance of the constructed database, we need target spectral
samples as measured ones. In this study, we referred to published measured spectral

97reflectance samples (459 curves) of Japanese women’s cheek which are included in SOCS
(standard object colour spectra) library [150] and commercially available. To test thesesamples as target spectra for validation experiments, we need knowledge on their input
values corresponding to the five simulation parameters for the nine layers. The SOCS
data do not contain this information. Instead of using directly 459 raw data in SOCS, weselected 459 substitutive simulated spectral data from the constructed database so thatthey are most similar to the 459 raw data in the SOCS one by one. Then, we chose a target
spectrum as a test data one by one from those of 459 selected data, and performed thedata mining from the database of 552,420 – 1 spectra by the criterion of the minimum
RMSE. Here we employed the leave-one-out method in the partial cross validation, so we
excluded the target spectrum itself in the mining process. The extracted spectrum seems
to be very similar to the target spectrum, but not the real target spectrum. This process
was repeated one by one for each the of 459 selected spectra. Each of the extracted spectra
holds its five magnification factors in the database. So, we compared values of the fivemagnification factors between the extracted and the target spectra and evaluated its
difference as an estimation error ∆Mc,L defined by∆𝑀𝑐,𝐿 =

∗
𝑀𝑐,𝐿
−𝑀𝑐,𝐿

𝐼𝐿

,

(4.2)

where M*c,L and Mc,L are magnification values of the target and extracted spectra,
respectively, subscript c indicates ‘a’ for absorption and ‘s’ for scattering, and L is a groupof layers. IL is a changing unit or interval of magnification which is the gap between two
consecutive discrete steps in each of the five magnification factors. This is used to divide
the difference between magnification values of the extracted and target spectra in Eq.

98

(4.2) so that the estimation error could be expressed in a normalized form and comparedamong all the five magnification groups that have different ranges of magnification values.
Figure 4.3 shows estimation error distributions of the 459 extracted magnification
values for the absorption coefficient in two groups and scattering coefficient in threegroups. The peak of the distribution was found to appear at the center (which indicateserror of 0%) in Figs. 4.3(a)-4.3(d). This indicates that the absorption and scattering
coefficients can be appropriately estimated for the target spectrum by using theconstructed database, except for the scattering coefficients in the deeper layers, L7- L9 in
Fig. 4.3(e). It seems difficult to estimate correctly s-value in the deeper three layers
because of experience of multiple scattering through relatively long path length ofphotons. In Figs. 4.3(a) and 4.3(c), the results show a case with no error, and indicate thatvalues of Ma,1-3 and Ms,1-3 for all the 459 extracted spectra are correctly assigned to thosefor the 459 target spectra. This is slightly questionable. It probably comes from the naturethat the absorption and scattering of light in the upper layers of the skin are strongly
reflected on the shape of reflectance spectrum. We consider that the same kind ofvalidation experiments should further be made with addition of noise in the near future.
For example, if a certain amount of noise is randomly added to the target spectrum, weconsider that our mining still extracts a similar spectrum from the database. So, themagnification factors are estimated from the extracted spectrum, but we may see thelarger estimation error due to the added noise. It is also noticed that the estimation error∆Mc,L in Eq.(4.2) is defined as a normalized value by the magnification interval IL given
in Table 4.2.

99

Number of samples

Number of samples∆Ma [-](a) Case of ∆Ma [-] in layers L1-L3∆Ma [-](b) Case of ∆Ma [-] in layers L4-L9
Fig. 4.3 Distribution diagrams of Ma and Ms.

100Number of samples
Number of samples∆Ms [-](c) Case of ∆Ms [-] in layers L1-L3

Number of samples∆Ms [-](d) Case of ∆Ms [-] in layers L4-L6∆Ms [-](e) Case of ∆Ms [-] in layers L7-L9
Fig. 4.3 Distribution diagrams of Ma and Ms.

101

4.4.4 Ambiguity Problem and Identification with PSF
Although the spectral database is promising for extraction of the simulated
spectrum similar to the measured spectrum, we found the problem that some different setsof a and s values produce the similar reflectance spectra in the database. This can be
an ambiguity problem for extracting correctly the desirable simulated spectrum from thedatabase. Figure 4.4 demonstrates a typical example of the three similar spectra, which
are simulated with values of a and s changed by different magnification factors given

Reflectance [%]in Table 4.3.

1
2
3

2

3

1

Wavelength [nm]Fig. 4.4 Examples of similar spectra simulated withdifferent values of Ma and Ms.

102

Table 4.3 Different magnification values of Ma and Ms for µa
and µs, respectively.

µa

M agnification valuesµs

Reflectancecurve

Ma,1-3Ma,4-9Ms,1-3Ms,4-6

Ms,7-9

1

1.1

0.4

0.8

0.3

0.6

2

1.3

1.6

0.8

4.8

0.1

3

1.4

2.2

1

9.3

0.2

To access this problem, we investigated changing behaviors of simulated spectra
and also a simulated point spread function (PSF) of reflected intensity on the surface ofthe skin model, with the above-mentioned five magnification groups. In general, the PSF
is known to reflect the scattering property of skin tissue. The PSF is simulated in thisstudy at wavelength of 700 nm under the condition of circular spot illumination with
Gaussian nature having a standard deviation of σ = 0.4 mm in the direction normal to theskin surface. We regarded this spot size as point-like illumination. If the PSF with a realpoint illumination is necessary, the deconvolution process can be used. Figures 4.5 and4.6 show typical spectra and PSFs with different values of Ma,1-3 and Ma,4-9, respectively.
In Fig. 4.5(a), the reflectance changes in the whole wavelength range which is probably103due to the effect of melanin. This type of spectral change is assigned to “Type A” here. In
Fig. 4.6(a), the reflectance demonstrated its large change especially in the middle
wavelength range of 500-600nm, and assigned to “Type B”. Almost no change is foundin PSFs of Figs. 4.5 and 4.6. This means that change of absorption coefficient a is notreflected on the PSF curves. Figures 4.7, 4.8, and 4.9 show typical spectra and PSFs
obtained by changing values of Ms,1-3, Ms,4-6, and Ms,7-9, respectively. It is found that thereflectance changes in a specific range of wavelength with respect to the three groups oflayers. The spectral changes by Ms,1-3 are found in the shorter wavelength range, those by
Ms,4-6 in the middle, and those by Ms,7-9 in the longer, and are respectively assigned to“Type C”, “Type D”, and “Type E” here. It should be here noticed that the two changing
types B and D of spectra seems to be very similar to each other in Figs. 4.6(a) and 4.8(a).
We consider that this can probably be a cause of producing the similar spectra. In Fig.
4.6(a), the reflectance decreases in the middle wavelength range with increasing value ofMa, while in Fig. 4.8(a), it decreases in the same range with decreasing value of Ms. The
magnification factor Ma,4-9 changes the value of absorption coefficients in the wholedermal layers and subcutaneous fat layers at the same time. But the primary effect by thischange probably occurs in the upper dermal layers of L4 – L6. Therefore, an increase or
decrease of a and s in the upper dermal layers makes an opposite effect on thereflectance in the middle wavelength range. This significant finding elucidates that it isdifficult to discriminate between the change of a and that of s in the upper dermis fromthe spectral reflectance changes. We considered that the broadened distribution of
estimation errors in Figs. 4.3(b) and 4.3(d) are also due to the same cause.

104

0.6

Ma = 0.6
0.8
1.0
1.3
1.6

Intensity [-]Reflectance [%]Ma = 0.6
0.8
1.0
1.3
1.6

1.6

Wavelength [nm]Distance x [mm](b) PSFs(a) Spectra

Ma = 0.2
1.0
2.0
3.0
3.6

0.2

Ma = 0.2
1.0
2.0
3.0
3.6

Intensity [-]Reflectance [%]Fig. 4.5 Typical spectra and PSFs in 700nm simulated withdifferent values of Ma,1-3.

3.6

Wavelength [nm](a) Spectra

Distance x [mm](b) PSFs

Fig. 4.6 Typical spectra and PSFs in 700nm simulated withdifferent values of Ma,4-9.

105

Ms = 0.6
0.8
1.0
1.2
1.4

Intensity [-]Reflectance [%]Ms = 0.6
0.8
1.0
1.2
1.4

1.4
0.6
Wavelength [nm](a) Spectra

Distance x [mm](b) PSFs

Ms = 0.3
0.9
3.0
5.7
9.3

9.3

0.3

Ms = 0.3
0.9
3.0
5.7
9.3

Intensity [-]Reflectance [%]Fig. 4.7 Typical spectra and PSFs in 700nm simulated withdifferent values of Ms,1-3.

0.3

9.3

Distance x [mm](b) PSFs

Wavelength [nm](a) Spectra

Fig. 4.8 Typical spectra and PSFs in 700nm simulated withdifferent values of Ms,4-6.

106

1.0
0.2

Ms = 0.2
0.4
0.6
0.8
1.0

Intensity [-]Reflectance [%]Ms = 0.2
0.4
0.6
0.8
1.0

Distance x [mm](b) PSFs

Wavelength [nm](a) Spectra

Fig. 4.9 Typical spectra and PSFs in 700nm simulated withdifferent values of Ms,7-9.

Results of the simulated PSFs show no change with Ms,1-3 and Ms,7-9 in Figs. 4.7
and 4.9, respectively, but demonstrate remarkable change with Ms,4-6 in Fig. 4.8. Here, we
found that PSF behavior shows different properties between Figs. 4.6 and 4.8. Change ofs by Ms,4-6 results in clearly different slopes in Fig. 4.8, but no change in the slope in Fig.
4.6. We interpret this difference by a step size, or mean free path of photon in theframework of the MCS. The step size s is given by

𝑠=−

ln(𝜉)
𝜇𝑎 + 𝜇𝑠

,

(4.3)

107

in the MCS of light propagation in the layered skin model [11], where  is a random
number between 0 and 1. As seen from Table 4.1, s is typically much larger than a in
the skin tissue. Thus, change of a-value has negligibly small effect on the step size s,
while change of s-value has a substantial amount of influence on s. Increase of s resultsin decrease of s, and this probably enhances multiple scattering near the illuminating point,
which corresponds to the origin of distance x in the PSF graph. This can be considered
as a source that the PSF curve becomes steep with increase of s by Ms,4-6. In Fig. 4.8(b),the full width at half maximum, FWHM decreases from 0.95 to 0.69 with increase of Ms,46 from 0.3 to 9.3 in the wavelength of 700 nm.

Figure 12 shows the intensity distribution maps simulated in the MCS of photon
fluence [11] by using the same condition as those in Fig. 4.6(b) and Fig. 4.8(b). In Fig.
4.10(I), the circular area of intensity distribution seems not to be affected by the changeof values in Ma,4-9 or the absorption coefficient a in dermis. In Fig. 4.10(II), it is foundthat the circular area with a certain level of intensity becomes small with increasing valuesof Ms,4-6 or the scattering coefficient s, and indicates agreement with the PSF property in
Fig. 4.8(b). From the present discussion, we consider that the database including PSF data
as well as reflectance spectra are useful for identification of the target spectrum. Probably,
the PSF curves at 700 nm is effective for improving the present ambiguity problem.
On actual measurements, it is necessary to obtain PSF data as well as the spectrum
to use the present identification procedure. Usually, this can be made, for example, by
hyper-spectral imaging with a point-like illumination. It should also be noticed that
discrimination of the five types A to E of spectral changes can be made by comparing thespectral shape between before and after the change of skin condition and by finding the108wavelength band that shows the reflectance change. For the similar spectral changes intypes B and D, PSF becomes effective to differentiate the similarity. Therefore, we should
develop a certain intelligent procedure for identification of a measured spectrum and skinparameters in the framework of the constructed database in the near future. One possible
example is considered briefly here. We first measure spectra from non-diseased anddiseased areas of the same person, or occasionally before and after getting diseased. After
that, we compare those spectra and assign the spectral change to Types A to E. If it
matches with B and D, we need to compare PSFs from diseased and non-diseased sites.
If a certain change is found in PSFs, it can be recognized as type D, and we may say thatthe skin condition changes in its scattering property and we adopt the extracted spectrum
having a change of the magnification factor Ms,4-6 for the estimation of optical properties
from the database. If no change is found in PSFs, it can be said as type B.

109

Distance y [mm]Distance y [mm]Distance x [mm](a) Ma,4-9 = 0.2(b) Ma,4-9 = 1.0

Distance y [mm]Distance x [mm]Distance x [mm](c) Ma,4-9 = 3.6(I)
Fig. 4.10 Simulated intensity distribution on the surface of the skin model.
(I) and (II) correspond to the two types B and D of spectral change
by increasing values of Ma,4-9 and Ms,4-6, respectively.

110

Distance y [mm]Distance y [mm]Distance x [mm](a) Ms,4-6 = 0.3(b) Ms,4-6 = 0.9

Distance y [mm]Distance x [mm]Distance x [mm](c) Ms,4-6 = 9.3(II)
Fig. 4.10 Simulated intensity distribution on the surface of the skin model.
(I) and (II) correspond to the two types B and D of spectral change
by increasing values of Ma,4-9 and Ms,4-6, respectively.

111

To evaluate accuracy of the absorption and scattering coefficients, a and sobtained by the proposed method in the actual situations, it is desired to use any
alternative method to estimate a and s in skin tissue and to compare the results. This isprobably a possible approach to investigate the estimation accuracy. There are several
evaluation methods, as we mentioned in Section 4.1, which can be used for this purpose.
Also, the optical coherence tomography (OCT), and some advanced microscope
techniques including confocal scanning laser microscopy [151] and polarization-resolved
second-harmonic microscopy [152] are promising means to observe tissue structuralchanges which are closely related to the scattering coefficient. The comparison study withsome of these possible techniques should be performed in the future to clarify theaccuracy on the proposed estimating method.

4.5 Conclusion
We constructed a skin reflectance spectral database in the framework of the Monte
Carlo simulation with the nine-layered skin tissue model. To realize the database in thereasonable size, we proposed the moderate grouping method for nine layers which enables
us to change values of the absorption and scattering coefficient, a and s in the ninelayers in the reduced numbers of combinations. To identify the desirable spectrum in theextraction from the database, we described also the use of PSF behaviors. Using thepresent identification procedure, we expect to extract the most desirable spectrum which
is well close to the target spectrum, and to evaluate a and s values associated with thespectrum in the database in a real time manner. This approach provides us to obtain112

information on a and s in the target skin tissue to be measured without multi-variate
analysis and related mathematical modeling. However, the accuracy of skin parameters
obtained through the database is not yet investigated in actual situations. This study
described numerical approaches and, therefore, we intend to enhance the demonstrate theusefulness of this approach by in vitro and in vivo experimental studies in the near future.

113

Chapter 5
Spectral Classification and Mining in Simulated Reflectance
Database for Detection of Absorption
Change in Skin Tissue

5.1 Introduction
In the previous chapter [153], we proposed the use of a database consisting ofvarious simulated spectra for estimating the absorption and scattering coefficients of ameasured spectrum, in which a simulated spectrum that closely resembles with themeasured spectrum is extracted out from the spectral database. The absorption andscattering coefficients associated with the extracted spectrum are regarded as estimatedabsorption and scattering coefficients of the measured spectrum. To construct the database,
we also introduced the moderate grouping method [134] for changing values ofabsorption and scattering coefficients in the nine layers. By this method, we constructed
the spectral reflectance database in the reasonable size while utilizing the merit of havingnine layers in skin tissue model. We treated nine layers as two groups of the first to thirdand fourth to ninth layers for the absorption coefficient, and three groups of the first to
third, fourth to sixth, and seventh to ninth layers for the scattering coefficient. Thus, wehave five different groups, each of which demonstrates its specific spectral change type,
due to the change of group-wise absorption or scattering coefficient. We demonstrated bysimulation study a possibility that a type of difference between spectra before and after a114certain skin conditional change can be classified to one of the five groups and used forspecifying a cause such as decrease or increase of absorption and/or scattering
coefficients. We consider that this step can be helpful for mining efficiently a candidate
spectrum from the database. There was, however, the problem of ambiguity or uncertainty
in identification of a simulated spectrum being similar to the measured spectrum, sincesome different combinations of absorption and scattering coefficients produce similarreflectance spectra [135].
For example, decrease of absorption coefficient and increase of scattering
coefficient in dermal layer cause similar spectral change. Thus, it is at present difficult todiscriminate between them from spectral shape. However, if we pay attention to some
application in which absorption coefficient changes are primarily of interest, we expect
to detect the change of absorption by spectral shape recognition with less effect of
scattering change. Then, we believe that this database method may be applied to analysisof chromophores such as melanin and hemoglobin, since they are known to be directly
related to absorption coefficients of skin tissue layers. This application of the database isconsidered undoubtedly valuable, as many researchers have actually made much effort toestimation of melanin and hemoglobin concentrations using their respective techniques[72,56-59, 73-76], which provide useful information on various skin conditions including
cancers, pigmentation, and tissue metabolism.
In this chapter, we propose a method of spectral classification and mining foranalyzing change of chromophore concentration in the skin tissue using the spectral
database. In order to confirm experimentally a feasibility of the spectral classification,
which we demonstrated only by numerical simulation [153], we performed experiments
using agarose-gel phantoms. We next performed pressure-cuff occlusion and hot water

115

immersion experiments for a human subject, to confirm ability of the proposed methodfor detecting change in hemoglobin concentration or absorption coefficients of dermaland subcutaneous layers. To discuss results obtained by the proposed method, we
compared the tendency of estimated parameters with those by the MRA method. To
counter the problem of ambiguity in extracting a similar simulated spectrum to themeasured spectrum, we investigated a probable approach of using a customized databasewhich consists of simulated spectra with variation in absorption (or scattering)coefficients while keeping fixed scattering (or absorption) coefficients. This approach is
expected to be useful for detecting change in chromophore concentration with assumingthat no considerable change takes place in scattering property. In case it is unknown
whether skin condition changes in absorption or scattering, we consider that the spectralclassification may become a useful step to judge of utilizing the customized database forspectral mining.

5.2

Database and Spectral Mining

5.2.1 Nine-Layered Skin Tissue Model and Monte Carlo Simulation
Human skin is known to have a complex structure as can be seen in Fig. 2.12(a)of chapter 2. Generally, a skin tissue model consists of three layers which are epidermis,
dermis and subcutaneous layers. However, this model comes in short in mimicking
various skin conditions aptly. One of such examples is that melanin in epidermis andhemoglobin in dermis have inhomogeneous distribution along the vertical direction in116

skin tissue. We used a nine-layered model [122] which was developed on the basis ofhistological classification as shown in Fig. 2.12(b). Typical thickness value of each layer
shown in Fig. 2.12 is obtained by modifying the data of Meglinski and Matcher [123],
assuming the human forearm skin. These layers are modeled to be in parallel and have
uniform scattering and absorption property within each of the nine layers. Absorption
coefficients are characterized by concentration of chromophores such as melanin in
epidermis and hemoglobin in dermis due to their absorption property. On the other hand,
scattering coefficients are characterized by mainly the scattering property of fiber
components such as collagen and elastin that exist in dermis and are responsible forelasticity of skin. Inclusion of blood vessels and capillary network are also contributed tothe absorption and scattering. The overall effect of these blood vessels is treated in termsof absorption coefficients for hemoglobin contained in blood, and scattering coefficientsfor location and formation of vessels and networks. In this way, the nine-layered modelis regarded to generate more realistic skin conditions than the conventional three-layered
model.
We employed Monte Carlo simulation (MCS) for emulating light propagation inskin tissue statistically [10,136-140]. In MCS, we used the algorithm by Wang et. al. [30],which requires values of five input parameters to be specified at each wavelength for eachlayers. These parameters are scattering coefficient µs, absorption coefficient µa,
anisotropy scattering parameter g, refractive index n, and thickness t. In addition, oxygen
saturation Sat-O2 is also needed to be given for specifying absorption coefficient µa inlayers L4 to L9, as the µa of layers L4 to L9 (dermis and subcutaneous layers) mainly
represents the absorption characteristics of hemoglobin. Anisotropy scattering parameterg is given by the average cosine of scattering phase function, and it ranges from -1 to +1

117

by representing backscattering to forward scattering. According to the algorithm by Wang
et. al., we utilized the characterization of g by Henyey-Greenstein function [110], as wellknown approximated form of the phase function. It is generally known that typical valuesof g take 0.7-0.8 in skin tissue and greater than 0.9 in blood. Each of the selected g values
are set by referring to the published data [153, 141-144]. The refractive index n has
essentially wavelength dependency, yet is assumed to be constant in the visible
wavelength range with reasonable simulation accuracy. In this study, we performed thesimulation of diffuse reflectance in the wavelength range of 400-700nm at an interval of
10nm, and set the number of photon to be 106 for stable simulation. To prepare typicalvalues for the five input parameters, we surveyed several articles [76,123,129,141-146]
that contain feasible values at specific wavelength points and then modified them to have
all the necessary values by interpolation and extrapolation. More detailed explanation forthis skin model and MCS is given in the previous work [122].

5.2.2 Construction of Database by Moderate GroupingTo estimate skin parameters of a measured spectrum, we proposed a method ofspectral database [153], in which a simulated spectrum that closely resembles with acertain measured spectrum is extracted by the criterion of root mean square error (RMSE).
By the input parameters having been used for producing the extracted spectrum, we obtain
estimated results as skin parameters of the measured spectrum. Here, we employ RMSE
defined by,

1

2
RMSE = √𝑚 ∑𝑚
𝑖=1{𝑅𝑠 (𝜆𝑖 ) − 𝑅𝑡 (𝜆𝑖 )}

118

,

(1)

where Rs(λi) and Rt(λi) are simulated and measured (target) spectra, λi denotes ith discrete
point of wavelength and m is the total number of wavelength points. Usually, we employ
the condition of RMSE≤1~3%, according to the number of extracted spectra as candidates.
To develop the database, we initiated a design of having a large number of spectral databy changing all the five parameters (µs, µa, g, n, and t) in each of nine layers. Thisapproach may provide an enriched spectral database with a variety of simulated spectra
but we noticed that it increased unnecessarily the size of database including unrealistic
spectral data that correspond to unrealistic skin conditions. Then, we prudently observed
the spectral reflectance library which includes typical spectral change behavior withvarying of the five parameters in each of nine layers [149]. After that, we considered µa
and µs probably as the primary factors for changing a spectral shape, although the otherthree parameters are related to some changes in spectra, but may not contribute in asignificant amount to change the shape. So, we assumed that g, n, and t take constantvalues, while they are set differently in each of the nine layers and, then, we changed only
values of µa and µs to generate various simulated spectra to construct the database. By thisassumption, the number of parameters to be changed is reduced from 45 (5 parameters in
9 layers) to 18 (2 in 9).
To form the database in a reasonable size and simple way with still keeping theunique advantage of having nine layers, we further introduced the moderate grouping
method [153]. To make those 18 parameters to be changed (µa and µs in 9 layers) easily,
we introduced magnification factors Ma and Ms with respect to µa and µs, respectively intheir group-wise changing. So the change of values for µa and µs are executed by varying
Ma and Ms, in terms of µa × Ma and µs × Ms, respectively. Since µa is known to be119

characterized by melanin in epidermis and hemoglobin in dermis and subcutaneous layers,
we set two groups of layers L1-L3 and L4-L9. Then, we changed the values of µa in thenine layers by magnification factors, termed as Ma,1-3 and Ma,4-9 for two groups. On theother hand, µs is supposed to be related with the morphological changes in tissue structure
having refractive index differences, and are here made into three groups of layers, L1-L3,
L4-L6 and L7-L9. We changed the values of µs in the nine layers by magnification factors
termed as Ms,1-3, Ms,4-6 and Ms,7-9 for three groups. This group-based changing enables us
to make the process for changing values of µa and µs in nine layers convenient and lesstime consuming. It should be noted that this group-based changing makes magnificationin each group consistent, while keeping the context of µa and µs having different values
in all the nine layers. The range of changing for these five groups is given in Table 5.1.
For Ma,1-3 and Ms,1-3, we have enlarged their range from our previous version of database[153] to cover a wide range of spectral variation due to change of µa and µs in layers L1
to L3.
We optimized a value of discrete step for changing the magnification to be 0.2 for
Ma,1-3 and Ma,4-9, 0.3 for Ms,1-3 and Ms,4-6, and 0.1 for Ms,7-9 from our experience made in
the previous work [153]. Then, the number of discrete steps for changing is 18 for Ma,1-3
and Ma,4-9, 31 for Ms,1-3 and Ms,4-6, and 10 for Ms,7-9, and resultantly this database contains
3,113,640 spectral curves. It should be noted that variation in a layer thickness t was notconsidered in the construction of this database, although the thickness t may change in
different individual and different parts of a body. So, the estimated µa and µs do not reflect
the effects of changing t, as well as changing n and g. However, we consider that thisapproach is able to detect the relative change of µa and/or µs values due to somephysiological change, for example that in normal to abnormal skin hemo-dynamics. As a120

special mention, oxygen saturation Sat-O2 is kept fixed at 80% in order to reduce thecomplexity in construction of database.

Table 5.1 Magnification values used for producing
database. IL is a discrete step where L
indicates layers in a group.

Layers

Ma

Ms

1~3

0.2 ~ 3.6
(I1-3= 0.20 )0.3 ~ 9.3
(I1-3= 0.30 )4~6

0.2 ~ 3.6
(I4-9= 0.20 )0.3 ~ 9.3
(I4-6= 0.30 )
0.1 ~ 1.0
(I7-9= 0.10 )7~9

5.2.3 Spectral Classification and Mining
While using the database for detecting skin condition change, we confronted theambiguity problem that different sets of µa and µs values produce similar spectra [135] toeach other in the database. We recognize this as an annoying situation for extracting
properly the desirable simulated spectrum from the database. To assess this situation, weinvestigated changing behavior of simulated spectra in the five different types [153]corresponding to the above-mentioned five groups and magnification factors, as shownin Fig. 5.1. Figures 5.1(a) and (b) show typical spectra with changing values of Ma,1-3 and121

Ma,4-9. In Fig. 5.1(a), change of reflectance (around 40%) is found in the whole
wavelength range due to the effect of melanin, and assigned as “Type-A”. In Fig. 5.1(b),it can be seen that the change of reflectance (of 20%) occurs mainly in a range of 500600nm which is due to the effect of hemoglobin [147]. This type of change is assigned to“Type-B”. Figures 5.1(c), (d) and (e) show typical spectra with different values of Ms,1-3,
Ms,4-6 and Ms,7-9, and are respectively assigned as “Type-C”, “Type-D” and “Type-E”. In
Fig. 5.1(c), a significant change in reflectance (of 20 to 40%) can be seen mainly in aregion of 400-600nm. Figure 5.1(d) depicts reflectance change (around 20%) particularlyin the middle wavelength range of 500-600nm, and spectra in Fig. 5.1(e) shows slightchange (by 5%) in the longer wavelength range. It should be noted that Type-B and TypeD show similar nature of changing reflectance in the range of 500-600nm with respect toincreasing Ma,4-9 and decreasing Ms,4-6, respectively. We consider this behavior to be apossible source of producing the similar spectra in the constructed database.

122

Reflectance [%]Reflectance [%]M a,1-3 = 0.2
0.8
1.6
2.6
3.6

0.2

M a,4-9 = 0.2
1.0
2.0
3.0
3.6

0.2

3.6

Wavelength [nm](a) Type A(b) Type B

9.3

0.3

M s,1-3 = 0.3
2.4
5.1
7.5
9.3

Reflectance [%]Wavelength [nm]M s,4-6 = 0.3
0.9
3.0
5.7
9.3

9.3

0.3

Wavelength [nm]Wavelength [nm](c) Type C(d) Type D

Reflectance [%]Reflectance [%]3.6

M s,7-9 = 0.2
0.4
0.6
0.8
1.0

1.0
0.2

Wavelength [nm](e) Type E

Fig. 5.1 Five types of change in shape of simulated spectra includedin the constructed full-size database.

123

In chapter 4, we showed possibility of using a point spread function (PSF) of
reflected intensity on the skin surface. However, accurate measurement of PSF is adifficult task generally due to some limitation in camera device, optical components andsystem. So, we did not proceed with the idea of using PSF to identify the similar spectra.
Thus, the presence of ambiguity in the constructed database remains to be solved in ourstudy. Currently, we are considering some possible approaches for specific applications.
One of them is to compare very carefully spectral shapes of Type-B and Type-D. In caseof Type-B by change of Ma,4-9, we observe very small change (less than 3%) in reflectancein the longer wavelength range (650-700nm), but for “Type-D” by change of Ms,4-6 there
are some visible difference (more than 7%) in that range. This observation has its own
drawback, as it depends on a conventional comparison of spectral shape between “TypeB” and “Type-D”, which is time consuming and unstable and often requires experience.
Another possible approach is using an application-based customized database. Here wefocus our attention to a µa-type customized database that consists of spectra obtained bychanging µa or Ma while µs or Ms is fixed in the nine layers. This can be applied to asituation where a change in chromophore concentration takes place. In this case, the actual
influence by absorption change is expected to be seen without significant interference byscattering change. In Sections 4 and 5, the applicability of µa-customized database ispresented and discussed by comparison with the original database in a full size. So, in thisstudy we refer to the original database, which is constructed by simulated spectra withvarying both µa and µs values, as a full size database in contrast to the customized database.
In case of general application without knowledge on change in absorption or
scattering, we consider the following two-step approach. The five types of spectral change
as shown in Fig. 5.1 is first referenced for all measured reflectance spectra, two of which124

have some difference in the shape before and after a certain change of skin condition.
Then, we compare spectral shape of the two. Due to the distinct changes in a specificwavelength range of types A, C and E, these types may easily be identified. But, in caseof similar spectral changes in types B and D, it is required to mention a probable cause ofchanging skin conditions (absorption or scattering) to avoid erroneous judgement on typeof spectral change. For this, we need to perform very careful observation of spectralchange, especially in the longer wavelength range of 650-700nm. After this spectralclassification in the five types, then we can employ a customized database, instead ofusing a full size database. This two-step approach of spectral classification and mining is
expected to be advantageous for improving the reliability of spectral mining from thedatabase for detection of absorption (or scattering in the future) change in skin tissue.

5.3

Phantom Experiments

5.3.1 Agarose-gel Phantom
To confirm the existence of five types of spectral change being used for spectralclassification, we performed experiments on agarose-gel type skin phantom [74,154]. We
fabricated a three-layered phantom model, where layers PL1, PL2 and PL3 correspond to
epidermis, dermis and subcutaneous fat tissue, respectively. Each of the three layers has
specific thickness: 0.6mm for PL1, 6.8mm for PL2 and 4.5mm for PL3. All of these
specific thickness values have been determined by investigating many different models
where different sets of thickness were used and finally the above-mentioned set was
accepted so that we could have spectral reflectance curve rather similar to a measured

125reflectance spectrum of human skin. In layer PL1, coffee solution is used as an absorberor a melanosome substitute. For layers PL2 and PL3, horse blood (Nihon Biotest Kenkyujo,
Saitama, Japan) with hematocrit of 44% was used as an absorber. We used 20%-Intralipos
solution (Otsuka Pharmaceutical Co. Ltd, Tokyo, Japan) with different volumeconcentration (V. C.) to include the scattering phenomenon in all the three layers
separately. As the base material, agar solution was prepared by mixing agar powder (Ina
Shokuhin Kogyo, Nagano, Japan) in saline with a weight ratio of 0.8%. We did not use
any deoxygenating agent (for example- Na2S2O4) [74] in layers PL2 and PL3, so we
assumed the oxygenation level of these layers to be 100%. Table 5.2 shows the volumeconcentration of coffee, blood and Intralipos of a certain phantom, which we referred asa phantom in the standard condition in this study.

Table 5.2 Amount of coffee, blood, Intralipos and agarose-gel solution
used for a phantom at the standard condition.

Layer

Coffee[ml] (V. C.)

Blood (44%of HCT)[ml] (V. C.)

Intralipos
20% sol.
[ml] (V. C.)

Agarose-gel
solution[ml] (V. C.)

Thickness[mm]

Epidermis(PL1)

1.8 (9%)0.0

3.0 (15%)

15.2 (76%)

0.6

Dermis(PL2)

0.0

0.4 (0.8%)

10.0 (20%)

39.6 (79%)

6.8

Subcutaneous
(PL3)

0.0

0.4 (1%)4.0 (10%)35.6 (89%)

4.5

126

To measure a reflectance spectrum, we used the experimental system shown in
Fig. 5.2 [122]. Light from a halogen lamp (Hayashi, LA-150UX), which covers a visible
wavelength range, was focused through a light guide and lens in such a manner that it
produces a spot of 4 mm on a surface of a measured sample. Diameter and focal lengthof lens were 54 and 100 mm, respectively. The diffuse light reflected from the sample,
which was placed at a sample port of an integrating sphere (Labsphere, RT-060-SF), wascollected by a fiber probe having a diameter of 400 µm at the detector port. A light trap
was attached to the integrating sphere to suppress specular reflectance component, so that
only the diffused reflected light is detected. The detecting region at the sample port wascircular in diameter of 22 mm. A multichannel spectrophotometer (Ocean optics, SD2000) was employed to measure a reflectance spectrum of the light transmitted from thedetector port in a visible wavelength range of 400-700nm with an interval of 10nm. Figure
5.3 shows a typical reflectance spectrum measured on a phantom in the standard condition.
The result seems resemble a typical spectrum of human skin and demonstrates low
spectral reflectance in the shorter wavelength range and high in the longer one, withhaving ‘W’-like characteristic coming from the oxygenated hemoglobin in the middle
wavelength [147].

127

SampleIntegrating sphere

Illuminated area
4 mm

Light trap

White light(Halogen
lamp)

Lens

Detector port

PC

Optical fiber100

Detection area
22 mm

Reflectance R [%]

80
60
40
20
0

400

500

600
wavelength [nm]700Spectrophotometer

Fig. 5.2 Schematic diagram of spectral reflectance measuring system.

128

Reflectance [%]Wavelength [nm]Fig. 5.3 Typical reflectance spectrum measured on a phantom
at the standard condition.

5.3.2 Five Types of Spectral Change in Phantoms
As is shown in Fig. 5.1, there are five types of change in spectra simulated byusing the five magnification factors in the constructed database. Here, we tried to find out
five types of change in spectra measured on phantoms by varying the amount of absorberand scatterer. It should be noted that simulated spectra in the database are based on thenine-layered skin model. But, in the case of phantom experiment, we chose to fabricate athree-layered model due to its easy fabrication and repeatability, and with reasonablesimilarity to human skin in a reflectance spectrum. Figure 5.4 demonstrates five typical
spectral changes of reflectance corresponding to specific changes in volume

129

concentration of coffee, blood and Intralipos in different layers of the phantom. Figure
5.4(a) represents a typical change of spectral reflectance due to the change of volumeconcentration of coffee in layer PL1 and termed as “Type-A”, following the corresponding
notation of Fig. 5.1. With increasing the amount of coffee, the spectral reflectance isdecreased (nearly 10%) in 430-530nm and 600-700nm regions, but almost no change in
530-600nm region was found. This is probably due to strong absorption of light by blood
component in layer PL2. Figure 5.4(b) shows a typical change of spectral reflectanceproduced by different volume concentration of blood in layers PL2 and PL3, and termed
as “Type-B”. Increasing of blood results in some decrease (around 10%) in reflectance in
400-500nm, and a substantial decrease (of 20%) in 500-600nm, and almost no change in
600-700nm. Figures 5.4(c), (d) and (e) show typical change in reflectance spectra withdifferent amount of Intralipos individually in layers PL1, PL2 and PL3, and termed as“Type-C”, “Type-D” and “Type-E”, respectively. In Fig. 5.4(c), increase of Intralipos inlayer PL1 resulted in increase of reflectance throughout the spectrum, especially withlarge increase (by 30 to 35%) in 400-580nm. In Fig. 5.4(d), increasing Intralipos
generated increase in reflectance throughout the spectrum, particularly (by 15 to 20%) ina region of 500-700nm. For the result in Fig. 5.4(e), almost no significant change wasfound in spectral shape with increase of Intralipos. It is probably due to the effect of
substantial absorption and scattering in layers PL1 and PL2. In this case, we consider thatthe travelling light does not reserve intensity large enough to penetrate deep into layer
PL3 and then come back to the surface.

130

Reflectance [%]Blood = 0.2ml
0.4ml
0.6ml
0.8ml

Wavelength [nm]Wavelength [nm](a) Type A(b) Type B

Reflectance [%]Intralipos = 1ml
3ml
5ml
7ml

Wavelength [nm]Intralipos = 5ml
7ml
10ml
17ml

Wavelength [nm](c) Type C

Reflectance [%]Reflectance [%]Reflectance [%]Coffee = 1.2ml
1.8ml
2.6ml
3.6ml

(d) Type D

Intralipos = 4ml
10ml
15ml
20ml

Wavelength [nm](e) Type E

Fig. 5.4 Five types of change in shape of spectra measured from phantoms.

131We compared these behaviors in the five types of spectral change in Fig. 5.4 withthose found in simulation of Fig. 5.1. For “Type-B” to “Type-E”, we can see relatively
similar change of pattern to each other between phantom and simulation. For “Type-A”,
there is obvious difference between the simulation and phantom. This was probably due
to mismatching in a changing range of absorption and scattering amounts between
phantom and simulation. To investigate this, we further simulated some spectra as
samples by changing µa in a small range with a single-layer magnification factor Ma,3 only
for layer L3 of the nine-layered model, instead of group-based change of µa in L1-L3 bymagnification factor Ma,1-3. The resultant spectra are shown in Fig. 6 and demonstratebetter similarity with that of “Type-A” for the phantom in Fig. 5.4(a), although somedifference still remains in 500-600nm. This investigation may additionally support that
careful observation of spectral change enables us to perform spectral classification into
the five types, since the spectral changing type of Fig. 5.5 is probably discriminated fromthe other types of “Type-B” to “Type-E” in Fig. 5.1.

132

Reflectance [%]M a,3 = 0.6
0.8
1.0
1.3
1.6

0.6

1.6

Wavelength [nm]Fig. 5.5 Simulated spectra with changing Ma,3 for layer L3 ofnine-layered skin model.

5.3.3 Blind Tests for Phantom
To validate possibility of classifying the types of change in spectral reflectance,
we performed a blind test for phantom samples in each of four types A, B, C and D, but
except for type E. We refer to a phantom in the standard condition given in Table 5.2 as
the standard phantom, whose reflectance spectrum is shown in Fig. 5.3. Here, we changed
concentrations of coffee, blood and Intralipos in four different cases. In the first case, wechanged only the concentration of coffee in layer PL1, which is referred to as a blindsample 1. Next, we changed that of blood in layers PL2 and PL3 together, referred to as ablind sample 2. Finally, we changed that of Intraliops in layer PL1 and layer PL2 separately,
and referred to as blind samples 3 and 4, respectively. So, all these samples resulted in 4
different spectral reflectance curves, which were compared against the spectral
133reflectance curve of the standard phantom to evaluate the types (A to D) of spectral change.
Figure 5.6(a) shows the comparison of spectra between the blind sample 1 and standard
phantom. The reflectance shows increase (of 5%) in 430-530nm and 600-700nm regions,but no change in 530-600nm, and this resembles “Type-A” of Fig. 5.4(a). So, this means
there must have been a decrease in volume concentration of coffee in layer PL1. This canbe confirmed by the fact that we only decreased the volume concentration of coffee inlayer PL1 from the standard value of 9% (1.8ml) to 3% (0.6ml). Figures 5.6(b) and 5.6(c)represent the comparison of spectrum from the standard phantom with that of blindsamples 2 and 3, respectively. These two results demonstrate some similarity in a changeof spectrum. Detailed observation may indicate that a notably significant change is foundin 500-600nm (around 15 to 18%) for Fig. 5.6(b), and in 400-580nm (around 20 to 25%)
for Fig. 5.6(c), which most likely resembles “Type-B” and “Type-C” in Fig. 5.6,
respectively. This observation agrees with the fact that we only decreased the volumeconcentration of blood in layers PL2 and PL3 from the standard value of 0.8% (0.4ml) to
0.2% (0.1ml) to fabricate blind sample 2, and increased the volume concentration of
Intralipos in layer PL1 from the standard value of 15% (3ml) to 40% (8ml) to fabricate
blind sample 3. Figure 5.6(d) shows large decrease in reflectance (by 15 to 18%) in thelonger wavelength (600-700nm) and small decrease (of 5%) in 450-600nm. These
characteristic traits are closely related to “Type-D”, which corresponds to behavior due
to decreasing amount of Intralipos in layer PL2. This prediction can be confirmed by the
fabricating condition in which the volume concentration of Intralipos in layer PL2 was
reduced from 20% (10ml) to 6% (3ml). For “Type-E” in a spectral shape of phantoms,
we did not perform any blind test. As we mentioned earlier, “Type-E” refers to thesituation of change in scattering property of layer PL3, and this effect seems very difficult134to be observed due to the presence of absorption and scattering in layer PL2. From thepresent blind tests, we confirmed the presence of five types of change in spectra and thepossibility of the spectral classification into those types in the framework of phantom
spectral behavior, although the spectral behavior of phantoms does not sufficiently

Reflectance [%]Standard condition
Blind sample 1

Standard condition
Blind sample 2

Wavelength [nm]Wavelength [nm](a) Blind test 1(b) Blind test 2

Standard condition
Blind sample 3Standard condition
Blind sample 4

Reflectance [%]Reflectance [%]Reflectance [%]coincide with that of real human skin.

Wavelength [nm]Wavelength [nm](c) Blind test 3(d) Blind test 4

Fig. 5.6 Typical examples of spectral change obtained in the blind test.

135

5.4

Pressure-Cuff Occlusion Experiment
To investigate applicability of spectral mining for detection of absorption change,

we performed pressure-cuff occlusion experiment, in which we expected to see changeof absorption coefficient µa in dermal and subcutaneous layers in a time-sequential
manner. A brachial cuff of a pressure type (aneroid) sphygmomanometer (Kenzmedico
Co. Ltd., Saitama, Japan) was fixed around an upper arm of a Japanese adult male andpressure up to 180mmHg was applied. We administered time-series measurement ofdiffuse reflectance spectra from the surface of inner side of a forearm using theexperimental setup shown in Fig. 5.2. The experimental protocol in Sects. 5.4 and 5.5(described later) were approved by the Human Research Ethics Committee of MuroranInstitute of Technology (Approval number H30-05-S05). The protocol for cuff occlusion
experiment consists of pre-occlusion for 60 seconds, cuff occlusion for 100 seconds at
the maximum pressure of 180mmHg and post-occlusion for 140 seconds in recovery to
the normal state. Each spectrum was obtained by averaging 15 runs with one run made in
160ms. As the duration of the measurement was confined to 300 seconds, 125 reflectancespectra were measured at the rate of one spectrum in around 2.4 seconds.
Figure 5.7(a) shows typical reflectance spectra selected over the time course of
300 seconds, in which a clear change by 3 to 6% can be seen in the wavelength region
larger than 500nm. To ensure increase or decrease of blood content with oxygenationlevel and no change of melanin content that we expected by cuff occlusion, we performed
the MRA [74,75] for measured 125 spectra, by following the method by Nishidate et. al.
[74]. According to modified Lambert-Beer law [60-62], an absorbance spectrum A(λ) canbe expressed as,136

𝐴(𝜆) = 𝐶𝑚 𝑙𝑒 (𝜆)𝜀𝑚 (𝜆) + 𝐶𝑜ℎ 𝑙𝑑 (𝜆)𝜀𝑜ℎ (𝜆) + 𝐶𝑑ℎ 𝑙𝑑 (𝜆)𝜀𝑑ℎ (𝜆) + 𝑆(𝜆) ,(2)where C and ε(λ) are the molar concentration and molar extinction coefficient, and le (λ)
and ld (λ) are the mean path length in epidermis and dermis, and S(λ) indicates attenuationby the other absorption and scattering of light. Subscripts m, oh and dh denote melanin,
oxygenated hemoglobin and deoxygenated hemoglobin, respectively. By using A(λ) as a
response variable and ε(λ) as predictor variables, MRA can be performed as

𝐴(𝜆) = 𝑎𝑚 𝜀𝑚 (𝜆) + 𝑎𝑜ℎ 𝜀𝑜ℎ (𝜆) + 𝑎𝑑ℎ 𝜀𝑑ℎ (𝜆) + 𝑎0 ,(3)

where am, aoh, adh and a0 are regression coefficients, and express the degree of contributionof each molar extinction coefficient to A(λ), being closely related to Cm, Coh and Cdh,
respectively. As am includes a factor of le (λ), and aoh and adh include a factor of ld (λ), thecoefficients am, aoh and adh have nonlinear relationship with Cm, Coh and Cdh, respectively.
To compensate this nonlinearity, Nishidate et. al. presented the use of conversion vectorsthat relate the regression coefficients am and ath (=aoh + adh ) to Cm and Cth (=Coh + Cdh ).
The estimated level of oxygen saturation Sat-O2 (%) is obtained by

Sat − O2 =𝑎𝑜ℎ× 100 .
𝑎𝑡ℎ

(4)

As cth represents concentration of total hemoglobin, it can be regarded as Cb or volumeconcentration of blood. Also, Cm can be regarded as Cmel or volume concentration ofmelanosome. Figure 5.7(b) depicts that Cb gradually increased by 27% and Sat-O2
apparently decreased by 37% under the occlusion, while almost no change in Cm

137

(fluctuation of 3 to 5%) in all the three stages of pre-occlusion, occlusion and postocclusion recovery. As soon as the occlusion was released, both Cb and Sat-O2 increased
sharply by 12% and 55%, respectively, in the moment and, then, slowly returned to theinitial values by respective decrease of 38% and 19%. This sharp increase in Cb and SatO2 is possibly due to the well-known effect of hyperemia, oxygenation and blood
circulation recovery [167].
Next, we referred to the types of spectral change in Fig. 5.1 to identify a type ofthe spectral change in Fig. 5.7(a). From visual observation of spectral shapes, a probabletype is considered to be types B or D or E. Since “Type-E” represents changing of
scattering in deeper layers of skin tissue, it is at present difficult to relate reliably such achange to spectral shape as noticed in the previous numerical investigation [153]. Thus,
we focus our discussion to “Type-B” or “Type-D”. At present, we have no appropriate
way to discriminate between these two types. Then, we performed spectral mining withthe constructed full-size database and extracted 125 simulated spectra for all of 125
measured spectra using criterion of the minimum RMSE. Magnification factors
associated with 125 extracted spectra are shown in Fig. 5.7(c), in which Ma,4-9 shows firstno change in pre-occlusion stage. After the occlusion starts, this factor increases by 39%
and reaches its maximum value and, then, saturates till the occlusion is withdrawn. Afterthe occlusion is over, Ma,4-9 decreases by 38.8% and returns to the initial value. No change
was found in the other three factors Ma,1-3, Ms,1-3 and Ms,4-6. No temporal response in thesefactors may assert that the spectral changes in Fig. 5.7(a) was mainly due to variation ofµa or hemoglobin concentration in the dermal and subcutaneous layers and corresponding
to “Type-B”.

138

Oxygen saturation [%]Volume concentration [%]Occlusion

Wavelength [nm]Time [sec](a) Examples of measured spectra(b) Estimated Cm, Cb and Sat-O2 by M RA

3.2

2.4

6
Occlusion

1.6

4

0.8

2

0

0

4

M agnification M a [-]Ma,1-3Ma,4-9 10
Ms,1-3Ms,4-6 8

4

M agnification M a [-]Cm /10Cb
Sat-O2

M agnification M s [-]Reflectance [%]2.4 sec
33.3 sec
59.5 sec
71.4 sec
119.0 sec
157.1 sec
166.6 sec
238.0 sec
300.0 sec

3.2
2.4

1.6
0.8

Occlusion

Ma,1-3Ma,4-90Time [sec]Time [sec](c) M agnifications by full-size database(d) M agnifications by customized database

Fig. 5.7 Results obtained by using MRA and spectral mining in thepressure-cuff experiment.

139

According to this experimental condition of cuff-occlusion also, we can assume
that the change of reflectance spectra in Fig. 5.7(a) is probably caused by variation ofhemoglobin concentration due to suppression of blood circulation. This assumption leads
to a prediction that the spectral change in Fig. 5.7(a) probably falls under the category of“Type-B” in Fig. 5.1(b). On the basis of this prediction, we conceived use of an alternative
database which aimed at searching for change of µa only. We constructed this customized
type of database by simulating spectra with different values of µa (with fixed Sat-O2 of80%) by changing Ma,1-3 and Ma,4-9 in the range of 0.2-3.6 with 35 discrete steps by intervalof 0.1, under constraint of µs value fixed at the standard condition. This resulted in 1225
simulated spectra with change in µa only, and was expected to have better estimation
specifically for change of absorption property corresponding to change of chromophore
concentration. Figure 5.7(d) shows time course of magnification factors extracted byusing the customized database, which represents better similarity of behavior between
Ma,4-9 and Cb in Fig. 5.7(b). It is also interesting that Ma,4-9 in Fig. 5.7(d) successfully
depicts a peak just after the release of occlusion. To express the change of Ma,4-9 in aquantitative manner, it first increased by 28% from its initial values under the occlusion,then further increased by 19% just after the release of occlusion, and finally returned tothe pre-occlusion values with a decrease of 47%. Small fluctuation (3 to 6%) in Ma,1-3 inFig. 5.7(d) is probably due to the improved resolution which became more sensitive tofluctuation in the measured spectra. We consider that this nature originates from micromovement and positioning errors of the measuring area on a forearm.
By comparing the results in Figs. 5.7(b) and (c) or (d), we can confirm theresemblance in the changes of blood concentration Cb and magnification factor Ma,4-9(with respective correlation coefficients of 0.962 and 0.905 for Ma,4-9 of the original and140customized database). Thus, the spectral mining from the database works well for
detection of change in absorption property. However, the database method is currently
not able to show the change of Sat-O2, since all spectra included in the database were
simulated with the fixed Sat-O2 level of 80%. In the near future, we intend to add data ofchanging Sat-O2 to the construction of database.

5.5

Hot Water Immersion Experiment
To assess further ability of the spectral mining for detecting change of absorption

condition, we performed an experiment on hot water immersion for a human forearm,where we expected to observe change mainly in absorption coefficient of dermal andsubcutaneous layers. This type of experiment is reported by some researchers to estimate
the change of hemoglobin concentration [57,58,128]. Again we employed theexperimental setup shown in Fig. 5.2. We first measured the reflectance spectrum on the
inner side of forearm of a Japanese adult male in a normal health state at t= 0 min. Then,we immersed his forearm inside a water tank having the average temperature about 45°C
for seven minutes. In two minutes, we performed reflectance measurement twice at t= 9
and 11 min. with an interval of two minutes. After that, we repeated measurement ofreflectance spectra eleven times at t= 16 to 66 min. with an interval of five minutes.
Finally, we obtained a total of 14 reflectance spectra in the time course of 66 minutes,
which are shown in Fig. 5.8(a). It is observed from Fig. 5.8(a) that reflectance is clearly
reduced around 10% in 500-600nm wavelength region under the influence of hot water
immersion. This property suggests increase of hemoglobin concentration or absorption
coefficient in dermal and subcutaneous layers. The reduction of reflectance is recovered141

as time passes on.
To understand this spectral transition, first we analyzed L* and a* values of these
14 measured spectra as visual evidence of change in skin conditions through theexperimental process. L* has a changing range from black to white representing decreaseor increase in brightness, and a* has a changing range from green to red. As we can see
from Fig. 5.8(b), after immersion L* value is decreased by 42% with referring to reduced
brightness and a* value is increased by 40% with referring to redness of skin due toincreased blood concentration. Then, both L* and a* values gradually increased by 34%
and decreased by 35%, respectively to their initial levels. Figure 5.8(c) shows calculatedcolor difference ∆E and RMSE (%) of measured spectra with respect to the first spectrum
at t= 0. These results indicate a change of skin color and spectral shape after the
immersion in terms of explicit increase of 97% and 48%, respectively, in the early timeand gradual decrease of 95% and 40%, respectively, in the latter. Figures 5.8(d), (e) and(f) which represent the photographs of the measuring forearm before and after the
immersion, further confirms the visual evidence. In Fig. 5.8(d), t= 0min represents before
immersion where both the measuring and non-measuring forearms shows similar color.
In Fig. 5.8(e), t= 11min represents four minutes after the immersion where increased
redness of the measuring forearm can be seen. In Fig. 5.8(f), t= 66min represents thephotograph of measuring forearm at the time of last measurement where no trace of theincreased redness can be found.
Figure 5.8(g) shows results of the MRA analysis (mentioned in Section 4) for 14
measured reflectance spectra. Both Sat-O2 and Cb increased sharply by 26% and 48%,
respectively, in the first eleven minutes and then decreased gradually by 38% and 44%,
respectively, but Cmel remains almost unaltered. This result indicates expectable behavior142

in the concentrations of hemoglobin and melanin as can be seen in the preceding articles[57,58,128]. It should be noted, however, that the MRA does not generally provide us theeffects of scattering properties distinctively, and thus, it tends to exhibit primarily theabsorption characteristics of melanin and hemoglobin.
We next extracted magnification factors for all the measured spectra using spectral
mining from the full-size database, as can be seen in Fig. 5.8(h). The result shows that
Ma,4-9 is increased rapidly by 61% just after the hot water immersion in the first elevenminutes and then returned nearly to its original value with a decrease of 45% over passing
time (having a correlation coefficient of 0.804 with Cb). However, for the spectra
measured at t= 31, 36 and 41 min., the extracted Ma,4-9 shows unexpected local increase(about 10 to 15%) in Fig. 5.8(h). This is probably due to the ambiguity defect [153] inspectral mining, which exists between the changing types B and D in spectral shape asshown in Fig. 1. This consideration may be supported to some extent by noting thatscattering magnification Ms,4-6 shows some decrease (by 13%) and then increase (by 22%)
during the experiment in Fig. 5.8(h). This is unexpected behavior since the experiment
was performed under the assumption of major change in absorption coefficient but almost
no change in scattering coefficient. So, we consider that spectral mining processerroneously accounted the change of spectral shape as the effect of scattering. Ma,1-3 andMs,1-3 exhibit no change in their values, and Ms,7-9 value is not shown here since the proper
representation of scattering in the deeper layers is difficult in the present approach.
To eliminate the effect of scattering in spectral mining, we again employed thecustomized database with µa-change only in the same way as the cuff occlusion
experiment in Section 5.4. Figure 5.8(i) depicts the absorbing magnification factors Ma
extracted from the µa-customized database for all 14 reflectance spectra measured in the143experiment. The µa-customized database rectifies the erroneous estimation ofmagnification factor Ma,4-9 obtained from the full-size database and provides reasonably
estimated Ma,4-9 values (having a correlation coefficient of 0.956 with Cb), which are
increased by 58% after the immersion and then gradually decreased by 53% with passing
time, while estimated Ma,1-3 values have no change. This improvement in the estimated
Ma,4-9 was probably made by a constraint that µa-customized database does not include
any effect of changing scattering property in the simulated spectra. So, this µa-type
customized database becomes more effective and useful in estimating the change ofabsorption coefficient or chromophore concentration than the full-size database.

144

Reflectance [%]

0 min
9 min
11 min16 min21 min26 min31 min36 min41 min46 min51 min56 min61 min66 minMeasured area

Wavelength [nm](a) M easured spectra(d) M easuring forearm At t= 0min

Measured area

a* [-]

L* [-]

L*a*

Time [min](b) Calculated L* and a* values(e) M easuring forearm At t= 11min

Measured area
∆E and RM SE (%)

∆E
RMSE

Time [min](c) ∆E and RM SE to the first spectrum(f) M easuring forearm At t= 66min

Fig. 5.8 Results obtained by using MRA and spectral mining in the
hot water immersion experiment.

145

Volume concentration [%]Oxygen saturation [%]Cm /10Cb
Sat-O2

M agnification M a [-]4Time [min]3.2

Ma,1-3Ma,4-9Ms,1-3 10
Ms,4-6
8

2.4

6

1.6

4

0.8

2

0

0

M agnification M a [-]4

M agnification M s [-](g) Estimated Cm, Cb and Sat-O2 by M RA

Ma,1-3Ma,4-9

3.2

2.4
1.6
0.8

0

Time [min](i) M agnifications by customized database

Time [min](h) M agnifications by full-size database

Fig. 5.8 Results obtained by using MRA and spectral mining in the
hot water immersion experiment.

In this chapter, we presented the possibility of estimating change of only
absorbing condition due to blood concentration. However, in the near future we intend to
do the same for change of melanin concentration and also scattering condition as well.
Our spectral mining from the database is capable of estimating unknown parameters ofmeasured spectra with moderate accuracy using both the full-size and customized
database, for the case where change of skin condition is distinguished between absorption
146

and scattering. However, for measured spectra without distinction, estimated parameters
may include some erroneous data due to the ambiguity present in the full-size database.
So, the ambiguity problem is yet to be solved by detailed investigation of spectral change,
which will be performed intensively in the near future.

5.6

Conclusion
We have described the method of spectral classification and spectral mining using

the database and validated its possibility to detect change of absorption in skin tissue
through the experiments with agarose-gel type skin phantom and on human forearm. We
also proposed the effectiveness of µa-type customized database for detecting the changeof absorption property for measured spectra with less influence by scattering effect. The
extracted parameters from the database showed expectable tendency in accordance withexperimental conditions. The results also agree with temporal response of skin parameters
obtained from the MRA. For target-known application such as changing of pigment
concentration, the present method can reasonably detect the change of parameters.
However, error may occur for general application due to the ambiguity problem.
Therefore, we continue to solve this subject with proper justification and reliability. We
should also investigate usefulness of the method for estimating changes of melaninconcentration and scattering property in the near future. The database approach has
flexibility in its own extension of contained spectral data and simplicity in determinationof probable skin change. In this respect, we consider that the present method isadvantageous for future development of on-line skin inspection. We expect that for such147

on-line skin inspection, from uploading measured spectrum to downloading skinparameter information, the whole process can be executed under a minute considering thecurrent size for both the original and customized databases.

148

Chapter 6
Concluding Remarks

The present dissertation has been dedicated towards the evaluation of opticalparameters of measured spectra using spectral mining in spectral reflectance database.
Chapter 2 was devoted to the description of fundamental theories and principles treatedthrough this dissertation. The constitution of biological tissue has been presented in
Section 2.2. The algorithm of the Monte Carlo simulation for the light transport in tissue
has been mentioned in Section 2.5. In Section 2.7, details of the layered skin model havebeen discussed. Spectral database is introduced in section 2.8. The composition of theskin tissue phantom used for experiments in Chapter 5 has been referred in Section 2.9.
Chapter 3 described the importance of appropriate aperture size for detection throughsimulative and experimental works. Chapters 4 and 5 consist of investigation of the
proposed database method. In chapter 4, the construction and numerical validation of thedatabase method has been discussed. Chapter 5 conveyed the experimental validation of
the database method to exhibit its usefulness.
Chapter 3 dealt with the effect of detection area for measurement of spectralreflectance. In this chapter, we describe how a reduced or inappropriate detection area
can influence spectral reflectance. This has been investigated by Monte Carlo simulationand experiments on human skin tissue using an integrating sphere. Section 3.3 highlighted
the numerical investigation, in which different illumination sources (point-like andcircular illuminations) are used to clarify the effect of the detection aperture in different
size through simulated reflectance spectra and photon fluence map in skin tissue model.

149

Also, in the same section, the longer wavelength of light penetrates deeper in a tissue
model producing broadening of diffuse light is reported. Section 3.4 confirmed thenumerical findings through the experiment on human forearm skin, in which a reduced
detecting aperture size resulted in significant decrease of spectral reflectance in the
wavelength range larger than 600nm. All these results indicated that an appropriate
detection aperture probably calculated by sum of an illumination spot size and a detection
margin of 4 to 6mm which generates from the broadening of diffused light.
Chapter 4 is allotted to the estimation of optical property, mainly absorption andscattering coefficients of a certain measured spectrum. Here, in order to do so, use of
database constructed with simulated spectral reflectance has been proposed, in which themost reasonable simulated spectrum is extracted out with a criterion of root mean squareerror. In Section 4.3.2, construction of database is explained in details, where moderategrouping for varying absorption coefficients (in two groups) and scattering coefficients(in three groups) with fixed thickness, refractive index and anisotropy scatteringparameter is introduced. This resulted in a convenient and simple way to build a databasewith keeping the advantage of nine-layered skin tissue model. Section 4.3.3 presented theperformance of the constructed database by numerical validation experiments whichindicated efficiency of the database to appropriately estimate absorption and scattering
coefficients, except for the scattering coefficients in deeper layers due to multiple
scattering experienced by photons in longer path lengths. Section 4.3.4 mentioned the useof point spread function of reflected intensity on the skin surface, to counter the ambiguityin the database which occurs as some different conditions of absorption and scattering
coefficients in dermal layer produced similar simulated spectra. Point spread function
showed varying slope for changing scattering coefficients in dermal layer but not for150

changing absorption coefficients in dermal layer which conveyed its effectiveness in
identification of ambiguity present in the database.
Chapter 5 extended the study conducted in Chapter 4. As in Chapter 4 moderategrouping of absorption and scattering coefficients resulted in five types of spectral change
in the database, in Section 5.2.2 of Chapter 6 the applicability of types of spectral change
in spectral mining to extract the most nearer simulated spectrum from spectral reflectance
database has been discussed. Also, these types of spectral change are validated from five
types of spectral change in measured spectra from agarose-gel phantom, described in
section 5.3. In Sections 5.4 and 5.5 pressure-cuff occlusion and hot water immersion
experiments are carried out, which confirms the effectiveness of the database method toevaluate change of absorption property in measured spectra, as under the experimental
condition absorption property in dermal and subcutaneous layers are expected to alter.
The results of the database method also compared with multiple regression analysis
method, which conveyed good resemblance. To counter the ambiguity problem in
database (full size or both absorption and scattering coefficients are varying), use of
application based customized database (for this study only varying absorption property)
is introduced, as measurement of point spread function is unreliable and has many
limitations.
As shown in the present dissertation, the spectral mining from spectral reflectance
database method has the ability to evaluate optical property of skin tissue in real time,
which enable it to analyze skin condition changes. Thus, it has the potential to become adiagnostic tool for clinician. The proposed database approach has flexibility in its own
expansion of contained spectral data and simplicity in determination of probable changes
in skin condition. The author expects that the results obtained in this dissertation will151

provide new possibilities for development of non-invasive, affordable and dependable
biomedical instrumentations in practical situations.

152

Appendix

A.1 Molar Extinction Coefficients of Hemoglobin
Tabulated Molar Extinction Coefficient for Hemoglobin in Water
These values for the molar extinction coefficient ε [cm-1/(moles/liter)] were compiled by
S. A. Prahl, Oregon Medical Laser Center, Portland.

λ

εHb02

εHb

(nm)

(cm-1/M)(cm-1/M)

400
410
420
430
440
450
460
470
480
490
500
510
520
530
540
550
560
570
580
590
600
610
620
630
640

26623246684048036024607210258062816
44480
33209.2
26629.2
23684.4
20932.8
20035.2
24202.4
39956.853236
43016
32613.244496
50104
14400.8
3200
1506
942
610
442

22329630395640756052860041328010329223388.8
16156.4
14550
16684
20862
25773.631589.6
39036.4
46592
53412
53788
45072
37020
28324.4
14677.2
9443.66509.65148.84345.2

650
660
670
680
690
700
710
720
730
740
750
760
770
780
790
796.8
800
810
820
830
840
850
860
870
880
890
900

153

368
319.6
294
277.6
276
290
314
348
390
446
518
586
650
710
756
794.72
816
864
916
974
1022
1058
1092
1128
1154
1178
1198

3750.123226.562795.122407.92
2051.96
1794.28
1540.481325.881102.21115.881405.24
1548.52
1311.881075.44890.8
794.72761.72717.08693.76693.04692.36691.32
694.32705.84726.44743.6
761.84

A.2 Scattering Coefficients of Hemoglobin
Scattering Coefficients of Hemoglobin
These values for the scattering coefficients of hemoglobin (Hct=45%) μs [cm-1] by
A. Roggan, M. Friebel, K. Dorschel, A. Hahn, and G. Muller,“Optical Properties of Circulating Human Blood in the Wavelength Range 400-2500
nm.”

λ
(nm)

μs
(cm-1)

380
390
400
410
420
430
440
450
460
470
480
490
500
510
520
530
540
550
560
570
580
590
600
610
620
630

404.9005941
384.9863807
365.0721673
352.251562
423.7620887
547.9355277
653.7461703
793.7331961
816.556118
789.513794
885.9076687
924.6111993
939.3893184
864.5980921
815.4556114
731.2141063
714.6073616
682.1136629
679.6045077
650.1872705
631.7798589
665.138298
639.534366
637.2597872
620.9552357
626.5880452

640
650
660
670
680
690
700
710
720
730
740
750
760
770
780
790
800
810
820
830
840
850
860
870
880
890
900

154

632.2208547
630.0781385
627.030429
616.057287
614.6948399
611.0647542
612.0373642
612.8444024
609.867284
603.567032
594.5956224
595.2939979
595.9923735
586.3907999
576.5524685
570.3579049
582.0101885
572.7927015
568.503105
560.5667665
557.9289214
562.3604932
536.1938156
529.4371012
519.1470664
510.7039
498.664357

A.3 Absorption Coefficients of Melanosome
Absorption Coefficients of Melanosome in Epidermis
These values for the absorption coefficients of melanosome μa [cm-1] by
S. L. Jacques,“Skin optics,” Oregon Medical Laser Center Monthly news and articles on Biomedical
Optics and Medical Lasers, http://omlc.ogi.edu/news/jan98/skinoptics.html, 1998.

λ
(nm)

μa
(cm-1)

400
410
420
430
440
450
460
470
480
490
500
510
520
530
540
550
560
570
580
590
600
610
620
630
640
650
660

1427.85519
1315.14516
1213.73478
1122.26128
1039.55273
964.59779
896.52077
834.56097
778.05557
726.42535
679.16269
635.82156
596.00898
559.37788
525.62101
494.46571
465.66951
439.01632
414.31315
391.38731
370.08397
350.26407
331.80247
314.58637
298.51394
283.49308
269.44035

670
680
690
700
710
720
730
740
750
760
770
780
790
796.8
800
810
820
830
840
850
860
870
880
890
900

155

256.28008
243.9435
232.36808
221.4968
211.27769
201.66323
192.60999
184.07818
176.03131
168.4359
161.26118
154.47881
148.06272
143.89665
141.98886
136.23504
130.78075
125.60701
120.69629
116.0323
111.59997
107.3853
103.37527
99.55778
95.92159

A.4 Scattering Coefficients of Epidermis and Dermis
Scattering Coefficients of Epidermis and Dermis
These values for the scattering coefficients of epidermis and dermis μs [cm-1] by
S. L. Jacques,“Skin optics,” Oregon Medical Laser Center Monthly news and articles on Biomedical
Optics and Medical Lasers, http://omlc.ogi.edu/news/jan98/skinoptics.html, 1998.

λ
(nm)

Μs
(cm-1)

400
410
420
430
440
450
460
470
480
490
500
510
520
530
540
550
560
570
580
590
600
610
620
630
640
650
660

390.625
363.3411
338.9208
316.99937
297.26636
279.45635
263.34173
248.72634
235.44091
223.33855
212.29166
202.18893
192.93294
184.43836
176.63008
169.4419
162.81535
156.69865
151.0458
145.81599
140.97272
136.48346
132.31923
128.45388
124.86409
121.52883
118.42932

670
680
690
700
710
720
730
740
750
760
770
780
790
796.8
800
810
820
830
840
850
860
870
880
890
900

156

115.54852
112.87106
110.38327
108.0726
104.19232
100.53299
97.07836
93.81345
90.72475
87.79972
85.02712
82.39644
79.89814
78.2704
77.52345
75.26424
73.11316
71.06328
69.10831
67.24232
65.46
63.75627
62.1265
60.56638
59.07186

A.5 Anisotropy Scattering parameter of Epidermis and Dermis
Anisotropy scattering parameter of epidermis and dermis
These values for the Anisotropy scattering parameter of epidermis and dermis
g [-] by M. J. C. van Gemert et. al., “Skin optics,” IEEE TRANSACTIONS ON
BIOMEDICAL ENGINEERING,Vol. 36, No. 12 (1989)pp1146-1154.

λ
(nm)

g
(-)

400
410
420
430
440
450
460
470
480
490
500
510
520
530
540
550
560
570
580
590
600
610
620
630
640
650
660
670

0.736
0.73890.74180.74470.74760.75050.75340.75630.75920.76210.765
0.76790.77080.77370.77660.77950.78240.78530.7882
0.79110.794
0.79690.79980.80270.80560.80850.81140.8143

680
690
700
710
720
730
740
750
760
770
780
790
796.8
800
810
820
830
840
850
860
870
880
890
900

157

0.81720.82010.823
0.82590.82880.83170.83460.83750.84040.84330.84620.84910.851070.852
0.85490.85780.86070.8636
0.86650.86940.87230.87520.8781
0.881

A.6 Molar Extinction Coefficients of melanin
These values for the molar extinction coefficient ε [cm-1/(moles/liter)] of melanin werecompiled by[1] S. L. Jacques, "Optical absorption of melanin,"
http://omlc.ogi.edu/spectra/melanin/index.html[2] T Sarna, RC Sealy, Photoinduced oxygen consumption in melanin systems. Action
spectra and quantum Yields for eumelanin and synthetic melanin. Photochem.
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λ
(nm)
400
410
420
430
440
450
460
470
480
490
500
510
520
530
540
550
560
570
580

εMel
(cm-1/M)
2529.86223
2394.40817
2266.20659
2144.8692
2030.02846
1921.33653
1818.46419
1721.09985
1628.9486
1541.73132
1459.18383
1381.0561
1307.1115
1237.12604
1170.88775
1108.19599
1048.86088
992.7027
939.55134

590
600
610
620
630
640
650
660
670
680
690
700
710
720
730
740
750
760
770
780
790

158

889.24581
841.63375
796.57094
753.92088
713.55439
675.34921
639.18962
604.96608
572.57494
541.91809
512.90268
485.4408
459.4493
434.84943
411.56669
389.53056
368.67428
348.9347
330.25201
312.56964
295.83401

λ
(nm)
800
810
820
830
840
850
860
870
880
890
900

εMel
(cm-1/M)
279.99444
265.00296
250.81415
237.38504
224.67496
212.6454
201.25992
190.48405
180.28514
170.6323
161.49629

159

Acknowledgement

I would like to express my sincere gratitude towards Professor Y. Aizu for his
valuable instruction and useful suggestions with profound supervision on all of thepresent work, and also I am indebted for his continuous guidance, much needed
perspective, and encouragement throughout the present work, without which thisdissertation would not have been possible. I wish to extend my appreciation to Associate
Professor T. Yuasa and Associate Professor H. Funamizu for kind encouragement anduseful discussions on the simulative study and imaging systems. Heartfelt
acknowledgement extended to Associate Professor I. Nishidate of Tokyo University ofAgriculture and Technology and Associate Professor T. Maeda of Kushiro National
Collage of Technology for fruitful discussions and advices on skin optics, skin phantom
and imaging. Also, I am grateful to all other members of the Instrumentation Systems
Laboratory, Division of Production and Systems Engineering, Muroran Institute ofTechnology, for various supports with which the present dissertation has beensuccessfully accomplished.

Kaustav Das

160

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List of Papers by the Author[1] T. Yuasa, Y. Kobori, K. Das, T. Maeda, H. Funamizu, and Y. Aizu, “Effects ofdetection area on measurements of spectral reflectance in human skin tissue” Asian
J. Physics. 27, 9-12, 503-513 (2018).
[2] K. Das, T. Yuasa, I. Nishidate, H. Funamizu, Y. Aizu, “Simulated reflectancespectra and point spread functions in database constructed by moderate groupingof nine layers in skin model” Opt. Rev. 27, 233–245 (2020).
[3] K. Das, T. Yuasa, T. Maeda, I. Nishidate, H. Funamizu, Y. Aizu, “Simple detectionof absorption change in skin tissue using simulated spectral reflectance database”
Measurement (Under review, 2020).

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