Bio-Spectral Imaging: A Review
Onur Özçelik

Sabri Bolkar

Supervisor: Asst. Prof. Sevinç Figen Öktem

Abstract
Spectral Imaging is a promising optical technique that combines advantages of both spectroscopy and imaging in an unique way. It offers
a noninvasive diagnostic method for numerous diseases especially for theones that originates from epithelial tissues. Herein, this report gives anintroductory overview of bio-spectral imaging and its main medical applications. It also briefly describes system hardware and main instrumentsthat constitute bio-spectral imaging devices.

Bio-Spectral Imaging: A Review

Contents
List of Figures

2

1 Introduction

3

2 Origins of Bio-Spectral Imaging

4

3 Hardware of Bio-Spectral Imaging Devices: Instruments

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4 Fundamentals of Bio-Spectral Imaging

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5 Classification of Imaging Systems
5.1 According to Spatial Scanning Methods . . . . . . . . . . . . . .
5.2 According to Spectroscopic Measurement Types . . . . . . . . . .
5.2.1 Near-infrared Spectral Imaging (NIR-SI) . . . . . . . . . .
5.2.2 Reflectance SI . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.3 Fluorescence SI . . . . . . . . . . . . . . . . . . . . . . . .
5.2.4 Light Scattering SI . . . . . . . . . . . . . . . . . . . . . .
5.2.5 Raman Scattering SI . . . . . . . . . . . . . . . . . . . . .

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6 Spectral Imaging Data Analysis
6.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3 Spectral Unmixing . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Conclusion

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8 References

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List of Figures
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3d Data Cube (Source: Zeiss-Campus) . . . . . . . . . . . . . . .
Spectral Scanning - comparison of RGB, multispectral and hyperspectral imaging methods . . . . . . . . . . . . . . . . . . . .
Timeline of three different categories of spectral imaging, alongwith synthetic aperture radar (SAR) imaging [3] . . . . . . . . .
SI system overview . . . . . . . . . . . . . . . . . . . . . . . . . .
Light Tissue Interaction [14] . . . . . . . . . . . . . . . . . . . . .
Data Processing Steps . . . . . . . . . . . . . . . . . . . . . . . .
Main classifier algorithms . . . . . . . . . . . . . . . . . . . . . .
Taxonomy of Unmixing Algorithms [27] . . . . . . . . . . . . . .

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Bio-Spectral Imaging: A Review

1

Introduction

Spectral Imaging is a very special imaging method which extracts characteristic spectral information of elements covering the imagined area. Unlike
other optical imaging methods (e.g., optical coherence tomography, optical diffuse tomography) which are monochromatic in their nature, spectral imaging
utilizes different band gaps of electromagnetic spectrum and combines themto acquire all the available chemical fingerprints of ingredients as much as thecurrent technology allows. Because of this reason, data in SI systems is stored
as 3D cubes (x, y, λ) rather than mere 2d monochromatic images. As put by
Qin, It can also be described as a very special multidimensional spectroscopy
device that applies spectroscopic measurement for each point in imagined area:”If conventional imaging tries to answer the question where and conventional
spectroscopy tries to answer the question what, then hyperspectral imaging tries
to answer the question where is what” [1].

Figure 1: 3d Data Cube (Source: Zeiss-Campus)
In the scientific literature, spectral imaging modality (i.e., imaging spectroscopy, once it was called) is divided into four main groups according toamount of spectral bands scanned. Names of research groups also indicate number of wavelengths utilized (i.e., Multispectral, hyperspectral and ultraspectral
imaging). The term multispectral imaging is used to express the method thatutilize only a few wavelengths to extract information. Hyperspectral imaging, Unlike multispectral imaging, includes hundreds of overlapping contagious
bands, and ensures no spectral bands are skipped. And, ultraspectral imaging
is much more advanced technique that includes thousands of contagious bands.
If conventional color cameras are to be compared with spectral imaging cameras, huge differences can be observed. Traditional cameras usually uses a single
imaging chip which consists of color filters covering every pixel of a sensor. Eachpixel of the sensor can detect only one of the three primary colors. Therefore,
three pixels are required to detect the color of the area. Color cameras record
three spectral bands simultaneously to obtain the color image and represent
the image with three parameters: Red-Green-Blue, or RGB (figure 2). Thiscameras emulates the human vision and easy to interpret. However, similar tohuman vision, they have limitations. They misses spectral information outsideof the three bands which is important for identifying a material. Therefore,color imaging systems cannot distinguish between different materials with thesame color. This significantly limits diagnostic capabilities of color cameras and3

Bio-Spectral Imaging: A Review

unveils the need for a more advanced system. This problem can be pointed as
the main motivation behind spectral imaging architecture. Since, they obtain
images at many wavelengths which are not recognizable by human vision andcarries unique information about many materials which are not available with
RGB cameras.

Figure 2: Spectral Scanning - comparison of RGB, multispectral and hyperspectral imaging methods

2

Origins of Bio-Spectral Imaging

The idea to use several spectral signatures of elements dates back to 1970’s
when a detailed analysis was needed for remote sensing applications (i.e., NASA
Landsat-1 in 1972 transferred intense spectral data). As put by Alexander
Goetz, who is the main figure behind this miraculous method, it was the enormous noisy data that led this great invention: ”No single material requires
hundreds of spectral bands spread over several octaves of the electromagnetic
spectrum to be identified uniquely. However, when mixed with many othermaterials on the Earth’s surface and viewed through a hostile and changing atmosphere, there is security in numbers.” But, at that time the problem was thelimited technology to analyze collected data since improvements in the field washighly stick to the advances in computational power. It was 90’s when spectral
imaging technique fully exploited [2]. A timeline for spectral imaging airborneand satellite cameras can be viewed in figure 3.
Before 90’s, remote sensing SI devices were not suitable for laboratory use
and for bio-applications because of their high cost, large size and complexity.
However, after late 80’s, advances in computational power and increase in availability of low cost sensors (e.g., CCD detectors) enabled biomedical implementation of spectral imaging technique [4]. One of the early successful applicationof the technique came with the name SpectraCubeTM 1000, developed in 1994
by a team from Bar Ilan University and Spectral-DiagnosticsTM [5]. It stands
as the first successful implementation of the idea. Although the system’s spatial resolution was quite low (just 170×170), it was able to observe subcellularcharacteristics of cells (i.e., visualization of chromatin packing, determinationof porphyrin level and distribution of cytoplasmic organelles). Later, spectral
imaging had become more common in biomedical society. Institutions started tocreate their own spectral imaging devices for many other diagnosis and research
purposes. More detailed analysis of these different spectral imaging systems will
be given in the following sections of the article.

4

Bio-Spectral Imaging: A Review

Figure 3: Timeline of three different categories of spectral imaging, along withsynthetic aperture radar (SAR) imaging [3]3

Hardware of Bio-Spectral Imaging Devices:
Instruments

A typical bio-spectral imaging system consists of five main instruments:
Light source to create information carrier signal interacting with matter, wavelength dispersion device to read spectral information from white light, imaging
detector such as CCD and CMOS area detectors, data analysis element (e.g.,
computer) and calibration module for spectral and spatial calibration (figure 4),
for most of the modern systems calibration module and other peripherals areembedded to the main computer to ease the control of the information flow.

Figure 4: SI system overview
The core part of the bio-spectral imaging architecture is the light source. Unlike its predecessor remote sensing hyperspectral imaging systems, bio-spectral
imaging devices uses active light to acquire knowledge from the sample (i.e., if
5Bio-Spectral Imaging: A Review

natural light sources are used, the system is called passive imaging system asin the case of most of the remote sensing applications). The main aim of thelight source is to create the means of interaction with material to be scanned.
Instruments utilized as imaging light sources are: Halogen lamps, light emitting
diodes (LED), lasers. There exist also another type of sources which are calledtunable sources. Systems using this approach are rather different with respect
to system architecture. They reverse the order and place dispersion element
before sample, so that matter interacting light become dispersed [1].
The system element dispersing broadband light to its monochromatic constituents is called wavelength dispersion device. Because imaging sensors are
physically color blind (i.e., they are only sensible to specific photon energy levels, only specific monochromatic light), broadband light has to be dispersed to beable to measure intensity level for each wavelength. Main instruments utilized
for this purpose are: Interferometers (e.g., Michelson Interferometer, Sagnac
Interferometer), imaging spectrographs, filter wheels, Acousto-optic tunable filters (AOTF), liquid crystal tunable filters (LCTF) and specialized dispersion
devices used in snapshot imaging methods [6] [1].
Third major component of bio-spectral imaging systems are area detectors.
After dispersion of spectral information carrier light, components of light are
directed to CCD and CMOS image sensors. They differ from traditional spectroscopy detectors and are able image an configured area (i.e., in spectroscopy,
point detectors are used).

4

Fundamentals of Bio-Spectral Imaging

Interaction of light with targeted tissue is the most important research topic
in bio-spectral imaging. As the information carrying medium, light should be
able to penetrate the tissue to reach the target, interrogate with cells and it
also should be recaptured by sensors to acquire related information [7]. In eachof these processes, there exist deep bio-physical interaction models and theories
which are above the scope of this article. But, it is essential to delve into
fundamental ideas about tissue optics and discussions why spectral imaging isa great choice of diagnosis technique.
There exist four main interactions of light photons with matter: Absorption,refraction, scattering and transmission, all of which occur at atomic scale andalter the state of light according to characteristic differences of interacted cells.
In addition to these fundamental processes, there are also some additional optical parameters that can be used to characterize tissues such as anisotropy (i.e.,
directional dependency of interacting materials) and reduced scattering [7]. The
first process light undergoes is absorption, the later processes are possible whenenergy level of light photon is not suitable for absorption, and photon escapesfrom first danger. After interacting with matter (e.g., scattered, refracted) photon’s energy level is lowered and probability of absorption increases. In all ofthese interactions light carries embedded fingerprints of the chemical structureeven in absorption. Since, after absorption increase in the energy of the material
may lead to re-emission or heat generation (even chemical reactions) that makes
materials observable [8].

6Bio-Spectral Imaging: A Review

Figure 5: Light Tissue Interaction [14]Most tissues in human body are heterogeneous; therefore, spatial variations
can be seen in their optical properties. Because of spatial variations, most oflight photons are scattered within human tissues [9]. The penetration depth oflight into tissues depends on how strongly light is absorbed by these tissues.
Most tissues cannot absorb enough light to permit it to penetrate significantlyin the spectral range from 600 to 1300 nanometer, known as diagnostic window.
Within this window, the propagating light becomes diffuse because scattering
is dominant over absorption [9] [10]. In tissues, absorption is a function ofmolecular composition, whose changes at specific wavelengths could provide aspectral fingerprint of the molecule for diagnosis [9] [11]. Light absorbed bytissues could be radiated as fluorescence. Ultraviolet and visible light usually
excite the molecules of the tissues and causes fluorescence emission. Different
fluorescence emission spectra could be observed in different cells which are indifferent disease states. Therefore, fluorescence imaging can be used to inspecttissues for medical diagnosis [10].

5Classification of Imaging Systems

5.1

According to Spatial Scanning Methods

Monochrome sensors used in spectral imaging systems can capture only twoof three spectral dimensions (x, y, λ) of the spectral cube at a time. Spatial or
spectral scanning methods are used to capture the third dimension [12]. The
whiskbroom, pushbroom, staring and snapshot (single exposure) methods arecommonly used in bio-spectral imaging applications [6].
In Whiskbroom method, spectrum (λ) of a single point is obtained by sensor
arrays at a time and the point is scanned along two spatial dimensions (x and y).
In Pushbroom method, one spatial dimension (x) and spectrum (λ) is obtainedwith a 2D detector array and the other spatial dimension (y) is captured withline scanning. Whiskbroom and Pushbroom methods require a lot time to acquire spectral cube because they do spatial scanning and intense data processing
is required. Another disadvantage of these methods is that they usually requirecomplex hardware configurations. In Whiskbroom and Pushbroom methods,
light is divided by a dispersive element (prism or grating), which makes them7

Bio-Spectral Imaging: A Review

efficient and low-cost [6].
Pushbroom method is suitable for searching an object with specific spectra,
because line-scan provides the entire spectrum of each pixel in real time. With
Pushbroom method, the entire spectral cube does not have to be collected.
For a known spectrum, a classified image can be assembled by identifying thespectrum in each spectral line scan [13]In Staring method, full spatial image (x and y) is obtained at once with a
2D sensor array while the spectrum (λ) is scanned. Instead of the dispersive
element used in Whiskbroom and Pushbroom methods, tunable filters are usedin this method and because of that, the spectral cube can be acquired in a
simpler way without any relative motion of the scene and the imager [15]. In
staring method, the number of spectral bands scanned can be determined by theuser and a dynamic range can be maintained [6]. Also, in clinical applications,staring spectral imagining devices become more suitable when imagined area is
exposed to wide field (broadband) illumination [12].
In Snapshot method, the spatial image (x and y) and the spectrum (λ) is
obtained in a single exposure. One of the advantages of the snapshot method
is that spectral image can be captured faster than other methods. Therefore,
it can be used in studies in endoscopy detection, fluorescent probes analysis
and retinal oxygen saturation measurement. Another advantage of this method
is high reliability and robustness since snapshot imagers has no moving parts.
It also provide high throughput because there are no multiplex losses. The
disadvantage of the snapshot method is low spatial resolution because the sizeof the spectral cube depends on the size of the CCD sensor used in the imager
[9].

5.2

According to Spectroscopic Measurement Types

The other type of classification is based on measurement methods. To collectspectral data from different type of tissues, several techniques have been developed. All of the methods will be discussed have various advantages over others,
especially when analyzing different tissues having unique optical characteristics.
Therefore, considerations related with optical properties of tissues are crucial
when choosing the optimal imaging modality.
5.2.1

Near-infrared Spectral Imaging (NIR-SI)

NIR spectroscopy has already been used in some applications such as measurement hemoglobin concentration and blood oxygen saturation [16]. Nearinfrared imaging can be used as a complementary method for tissue characterization and diagnosis of cancer because of its ability to distinguishing normal
tissue from diseased tissue. NIR light between 700 and 900 nm is mostly scattered in tissues and it can reach deep tissues. Unlike the other imaging methods
such as X-ray and MRI, extra harmful radiation or radioactive materials are
not used in NIR imaging [17].

8Bio-Spectral Imaging: A Review

5.2.2

Reflectance SI

Diseased tissues interact with light in some ways like elastic scattering andabsorption. When light interacts with a diseased tissue, the effect of the diseaseon the tissue causes some changes in the patterns in the light. These changes
can be seen on the light reflected from the tissue. There are several studies
investigating the applications of reflectance spectroscopy for tissues in cervix,
breast, oral cavity, lungs, esophagus and gastrointestinal tract. There are also
applications of reflectance spectroscopy for brain tumors, ovarian cancer, bladder cancer and skin cancer [18].
5.2.3

Fluorescence SI

Fluorescence is used in a wide range of applications such as fluorescence microscopy, DNA sequencing, flow cytometry and fluorescence-based immunoassays. In biomedical applications, fluorescence spectroscopy has been being investigated for applications such as characterizing cardiac tissue, analyzing bloodand early detection of cancer, particularly those in the cervix, oral cavity, breast,
brain, skin, esophagus and bronchus [19].
5.2.4Light Scattering SI

Light scattering spectroscopy (LSS) allows investigating the structure ofepithelial cells without removing them. With the use of LSS, Images of somehistological properties (e.g., nuclear enlargement, pleomorphism and increased
chromatin content) can be obtained. LSS imaging shows promising results forinvestigation of nuclear changes resulting from cancer and other diseases, particularly cancer in cervix and oral cavity [20]. Light scattering spectroscopy also
shows promise for the precise classification of invisible dysplasia in real time
[21].
5.2.5

Raman Scattering SI

Raman spectroscopy can provide information about chemical and molecularproperties of tissues without destructive effects and external contrast-enhancing
substances. Most of the reported studies about Raman spectroscopy examined
its applications in skin, although there are studies investigating its applications
in bone, brain, gastrointestinal tract, artery, breast, urinary tract, uterus, lung,and eye [22]. Since it consists of a lot of components (e.g., lasers, filters, microscope objectives), Raman spectroscopy is compatible with other methods ofbiomedical optics such as confocal laser-scanning, optical coherence tomography
and angularly resolved elastic scattering [23].

6Spectral Imaging Data Analysis

It can be stated that diagnosis process and medical treatment cannot be
possible without processing of acquired spectral data. Complexity and noisinessof raw data makes it cumbersome to comprehend even for expert physicians.
Therefore, to extract relevant feature, data is processed. General procedure forprocessing can be viewed in figure 6.

9Bio-Spectral Imaging: A Review

Figure 6: Data Processing Steps

6.1

Preprocessing

The first step in data processing is preprocessing. As the name implies
preprocessing is preparation step before real analysis is done. The data acquiredby SI devices include thousands of pixels and wavelengths. Processing of highdimensional data is harder and inefficient (i.e., classification algorithms check
unneeded data), therefore various preprocessing methods have been developedby engineers. In addition to its main aim easing the way to extract features,
preprocessing also serves for another purpose that is decreasing so-called curseof dimensionality [12].
Curse of dimensionality is accepted as one of the most important issues inmachine learning field. The term was coined in 1961 by Richard Bellman while
he was working of dynamic optimization [24]. This infamous term has been
popularized because of its counter-intuitive results. Increasing data dimension
may lead to expectation of more accurate results, but in contrast it is the highdimensional data itself scrambles the classifier. This strange outcome is themain reason for necessity of a preprocessor to lower data dimension. It should bestated that preprocessing algorithms are optimized not to lose any information
while decreasing dimension of data [25]. Principal component analysis is one of
the main methods for preprocessing applications, there exists also an advanced
form of PCA called independent component analysis (ICA) which can also be
used as preprocessor, but it’s mainly used as classifier especially for brain signal
analysis. A detailed and simplified information about PCA can be found in
Shlen’s famous tutorial [26].

6.2Classification

After removal of redundancy in data by preprocessing algorithms, remaining
data space is fed to classifier. Classifier is the main part in machine learning
systems where data is separated according to their feature components each having unique characteristics. There are various ways to do that job, but classifiers
are generally divided into two groups: Supervised Clustering and Unsupervised
Clustering.

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Bio-Spectral Imaging: A Review

Figure 7: Main classifier algorithms
Both supervised and unsupervised methods depends upon statistical resemblance mapping in which data points are selected and classified to maximize (orminimize) targeted statistical parameter. Discussion related with classification
algorithms is out of the scope of this paper because of intense research doneon machine learning algorithms. But, some of the most used algorithms can beviewed in Figure 7.

6.3

Spectral Unmixing

Distribution of materials in spectral data cube is generally heterogeneous;and a single pixel does not contain just one type of material. Therefore, there
is a need for sub-pixel analysis. The method to extract sub-pixel features iscalled Spectral Unmixing. It is used to approximate material ingredients using
known spectral of materials and pixel’s spectra. In other words, it is in a way a
sub-pixel spectral decomposer for spectral imaging systems [27].

Figure 8: Taxonomy of Unmixing Algorithms [27]11Bio-Spectral Imaging: A Review

7

Conclusion

We have presented mainstream spectral imaging applications in medical
diagnosis and treatment. The article is aimed to be an introductory text for
explaining general concepts without going into too much detail. In addition to
medical applications and spectral imaging methods, SI instruments, brief historyof hyperspectral imaging and data analysis techniques are also discussed. We
also referenced most cited and inclusive articles that fit best for beginners in the
bio-spectral imaging research field, therefore further investigation can be easily
done through reading sources referenced.

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