NBSIR 78-1442

Algorithms for Image Analysis

of Wood Pulp Fibers

Russell A. Kirsch

Institute for Basic Standards

National Bureau of Standards

Washington, D C. 20234

January 1978

Final

Paper presented at

1 978 Annual Convention of

Technical Association of the

Pulp and Paper Industries,

March, 1978

NBSIR 78-1442

ALGORITHMS FOR IMAGE ANALYSIS

OF WOOD PULP FIBERS

Russel A. Kirsch

Institute for Basic Standards

National Bureau of Standards

Washington, D C. 20234

January 1978

Final

Paper presented at 1978 Annual Convention of

Technical Association of the

Pulp and Paper Industries,

March, 1978

U.S. DEPARTMENT OF COMMERCE, Juanita M. Kreps, Secretary

Dr. Sidney Harman, Under Secretary

Jordan J. Baruch, Assistant Secretary for Science and Technology

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Acting Director
;
TABLE OF CONTENTS

Page

List of Figures and Tables i

Abstract iii

Introduction 1

Fiber Morphology Image Data Acquisition 3

Algorithms for Analysis of Fiber Images 9

Pattern Recognition Algorithms 19

Conclusion 23

Acknowledgements 23

References 2k

LIST OF FIGURES MD TABLES

Page

Figure 1: Photomicrograph of Southern Softwood

Pulp Fibers 4

Figure 2: Computer display showing manually traced

points of Figure 1 4

Figure 3: Coordinates (micrometers) of points traced

on a single fiber 5

Figure 4: Computer display of scanned fiber photomicrograph showing, with 10 distinct

characters, the range of 256 scan density

levels T

Figure 5: Computer display of full spatial resolution of scanned photomicrograph 8

Figure 6: Distances between tracing points in single

fiber 10

Figure 7: Ratios of length along fiber segments to

distances between end points. Distances

are calculated from lower left fiber end . . 10

Figure 8: Angle of bend (degrees) at each tracing

point along fiber 11

Figure 9 - Radius (micrometers) of circle passing

through each tracing point and adjacent

point on each side 12

Figure 10: Segment lengths between bends sharper than

10 degrees l4

Figure 11: Segment lengths between bends sharper than

20 degrees 15

Figure 12: Segment lengths between bends sharper than

"30 degrees 15

Figure 13: Histogram of number of fibers in length

classes of 0.1 mm. for three different pulp

sources l6

Figure l4: Histogram of number of fiber segments as

a function of segment length for curl angles

from 180 degrees (front) to 0 degrees (rear).

Data from 620 Northern Softwood fibers .... 17

i
LIST OF FIGURES AND TABLES (cont.)

Figure 15:

Figure l6:

Figure 17:

Figure 18:

Figure 19:

Page

Histogram of number of fiber segments as a function’ of segment length for curl

angles from 180 degrees (front) to 0

degrees (rear). Data from 1+25 Southern

Softwood fibers 17

Histogram of number of fiber segments as a function of segment length for curl

angles from l80 degrees (front) to 0

degrees (rear). Data from 15^+0 Hardwood

fibers l8

Boundary points of single connected blob

extracted from Figure h by thresholding at mean density, 157 20

Boundary points of single connected blob

extracted from Figure h by thresholding

at slightly higher density, 158 20

Boundary points of single connected blob

extracted from Figure 4 by thresholding at mean density plus 1.0 std. dev., l6U 21

Table 1: Angle of bend (degrees) (left col.) as a function of radius of curvature (micrometers)

(right col. ) of the tracing points in

Figures 8 & 9 13

li
ALGORITHMS FOR IMAGE ANALYSIS OF WOOD PULP FIBERS*

Russell A. Kirsch

National Bureau of Standards

Washington, D.C. 2023*+

ABSTRACT

Image analysis technology can he used to measure the visible

morphology of pulp fibers. But before such measurements can be ac- cepted, it is necessary to achieve precise definition of the necessary

measurements in the form of suitable algorithms that have been experimentally tested on images of actual fiber data. We present such

measurement results on both semiautomatically traced fiber data and on automatically scanned images. We explore the variety of definitions

possible for some simple well known properties of wood fiber morphology

by applying suitable algorithms to fiber image data. Finally, we suggest that this exploratory approach to the specification of the

precise image analysis measurements needed in paper manufacturing can facilitate the introduction of a technology for process control that

will result in savings in paper manufacturing cost and in reduction of

energy requirements.

Key words: Algorithms; artificial intelligence; image analysis;

morphological analysis; paper fibers; pattern recognition; pulp

characterization.

*This work was done, in part, under interagency agreement

EA-77-A-01-6010 A019-IEC between the Department of Energy and the

National Bureau of Standards.

iii
.

.
INTRODUCTION

The properties of manufactured paper are largely determined by the

physical and chemical properties of the wood pulp fibers used in its

manufacture. To a lesser extent, so are the energy costs of manufacturing the paper. To control the quality of the pulp raw material is to

control a large part of the manufacturing cost and the resulting physical properties of paper.

This control can be achieved in two very different ways: (a) by

holding substantially constant those pulp properties which affect costs

and paper properties, or (b) by making measurements on less well con- trolled pulps, thereby enabling compensation to be made during manufacture for the inevitable variation in pulp quality. Although many of

the measurements conventionally made on pulps are well developed and

well understood, they are necessarily of use only retrospectively in the

laboratory and consequently of limited use for process control because

of the time needed to perform them.

Many important measurements made in the laboratory are directed to

the visible morphology of pulp fibers. Such measurements are prime

candidates for the application of automatic image analysis technology.

This technology, now over twenty years old , 1 has matured to the point

where there are many commercial instruments available , 2 some at quite

modest costs compared to the costs of performing such measurements only

a few years ago. Most uses of this technology have been in fields

different from fiber morphology measurement. But where image analysis

has been used, there are enough similarities to warrant the investigation of the possible extension of the technology to the analysis of

pulp fiber morphology. Such fields include earth satellite image

analysis, biological microscopy, quantitative metallography, and a spectrum of lesser developed fields ranging from commercial to research

uses

In each of the fields where image analysis technology has been

successfully used, there has been an initial exploratory phase during

which the development of the particu lar types of measurements needed

used general purpose instruments. More specialized and economical

instrumentation has followed this exploratory phase. Today, the technology for development use is readily available. If an understanding of

image analysis technology’s potential for developing suitable fiber mor- phology measurements becomes widespread, one can anticipate a rapid

acceptance of image analysis technology by the pulp and paper industry

with the consequent benefits of reduced energy costs and improved quality control. It is to this challenge for new applications of image

analysis to the measurement of fiber morphology that we direct our attention in this paper. In a broader context than that of this paper, we hope to show how measurement methods using image analysis and pattern

recognition can be used to predict paper properties. The measurement

methods will be tested, at the National Bureau of Standards Paper

Laboratory. Those measurements that survive the tests by usefully

1
predicting paper properties will be made available to the electronics

industry, in the form of algorithms, to be transferred to more specialized electronic instrumentation. Although this transfer will involve

substantial improvements (to be made by others), it is expected that the

algorithmic basis for the measurements that will have been developed and

verified experimentally, will remain substantially unchanged. Thus, our contribution is to supply tested measurement algorithms for fiber

morphology. The present paper is devoted to the algorithm development.

Subsequent laboratory validation will be reported elsewhere.

In determining the morphology of pulp fibers, it is useful separately to consider the two stages of morphological measurement: data

acquisition, and algorithmic derivation of the desired properties from

the data. The first stage consists of using suitable transducers to

make measurements that are directly connected with simple geometric

properties of the image being measured. Thus such properties as the

coordinate locations (in some appropriate coordinate system) of points

in an image are determinations that belong to the data acquisition

stage. The second stage consists of performing algorithm based operations upon the results of the first stage to determine properties that

are usually more macroscopic and complex. Thus length measurements

belong to this algorithmic derivation stage.

The reason for distinguishing these two stages is that the data

acquisition stage is usually concerned more with physical properties of

the apparatus for making measurements whereas the algorithmic derivation

stage is usually concerned more with the algorithms (i.e., programs,

procedures) for calculating derived properties. Usually, the precision

of measurements is significantly determined by how the first stage is

conducted whereas the gross structure is determined by the second stage.

We would thus expect, that in areas where the measurements are well

defined and widely accepted as universal procedures, emphasis is placed

(and properly so) upon the data acquisition stage. But where there is

little agreement upon the nature of the kinds of measurements to be

made, emphasis must be placed on the formulation of suitable algorithms

which can serve as a basis for agreement upon which later measurements

can be based.

We will see examples, below, of both stages of morphological

measurement. For the first stage, we will present measurements made

both with graphic stylus semiautomated techniques and with automatic

scanning directly from image data. For the second stage, we will show

how different algorithms, operating upon the same set of data, can be

used to characterize a particular morphological property, that of fiber

curl.

An additional reason for considering the second stage of algorithmic

determination in a comparatively new area like pulp fiber morphology

measurement is that in the development of new measurement methods this

second stage probably should be considered first. It is all too easy to

adopt a technology for data acquisition and then to allow those

2
measurements which follow directly from the adopted technology to assume positions of importance in the measurement scheme. This has

always been the classical approach in scientific measurement. But the

existence of general purpose programmable computers as mediators in

scientific measurement has opened up wide new possibilities for producing measurements from instruments where the data furnished by the

instruments are by no means direct derivations from the instrument

transducers. Rather, they are the results of elaborate and sometimes

ill-understood computations. To have a firm algorithmic base for these

derived measurements is to insure that the results of the underlying

elaborate computations will not be misunderstood. It is interesting to

note that in the area of image analysis technology we already have

routine occurrences of investigators 'looking* at images that have never been seen before and that could not even, in principle, be 'seen' in any

simple sense of the word. A brain scan in a tomograph, a false color

satellite image of the earth, and even a scanning electron micrograph

all present a misleadingly simple picture that requires intimate knowledge of the algorithms underlying the instrument in the first two cases,

and of the transducer properties on the third, before one can hazard an interpretation of the simple image presented by the instrument. Since

the application of image analysis to fiber morphology measurement is so new, we will devote a correspondingly larger part of our discussion,

below, to these algorithmic derived measurements.

FIBER MORPHOLOGY IMAGE DATA ACQUISITION

There are several aspects of the morphology of pulp fibers that can be determined with suitable scanning methods. These include both

optical and electron microscopy based scanning methods. We will be

concerned here only with optical scanning techniques although some of

the methods described below will be equally applicable to measurements

that can be performed on electron microscope images. For purposes of

exploring the algorithmic base for fiber measurements, a semi-automatic

data acquisition method is first presented here. Subsequently we will

discuss methods for direct scanning of microscope images of fibers.

The semi-automatic method uses computer graphics technology. A

photomicrograph of a suitable pulp preparation is placed on a graphic

tablet. This tablet is connected to a general purpose programmable

computer which can accept indications, from a crosshair cursor, of the

coordinate locations being pointed to by a human operator. The operator

places this cursor, successively, on locations along the length of a particular fiber that he has chosen to measure in the photomicrograph.

Thus the operator describes a particular fiber to the computer which

represents that fiber as a sequence of (x,y) coordinate pairs. Each

of the fibers in the photomicrograph is manually described in this way

to the computer. For a photomicrograph, such as that shown in Fig. 1,

the process of tracing all the fibers takes slightly less than 10

minutes, the time being related to the number of measurement points

selected by the operator. In the case of Figure 1, this yields the

3
Figure 1: Photomicrograph of Southern Softwood Pulp Fibers

Figure 2: Computer display showing manually traced points of

Figure 1

4
points shown marked on the computer display of the measured photomicrograph in Fig. 2. The tablet used for the present experiments is capable

of resolving crosshair locations to within 0.25 mm in both x and y

directions. Of course, the resolution in terms of fiber dimensions is a function of the photographic magnification used.

We see, in Fig. 3, a particular fiber selected from the data of

Fig. 2 in which the coordinates of points chosen by the operator in

tracing that fiber are given. The original coordinate system used is an arbitrary one imposed by the tablet. Since the actual fiber length from

end to end as measured with a calibrated microscope reticle is 2.02 mm,

the coordinates have been converted to units of micrometers, which are the ones appearing in Fig. 3.

Figure 3: Coordinates (micrometers) of points traced on a single

fiber

This semi-automatic method of data acquisition allows us to define

a number of useful algorithm-derived measurements described below.

However, the measurements made are significantly dependent upon the

vagaries of manual placement of a tracing cursor and the underlying

decisions made by an operator while doing the tracing. It is necessary

to develop a fully automatic method for data acquisition if we are to be

able to insure reproduceability of any measurements made. For this

approach, automatic image scanning is necessary.

5
There are a number of different types of image scanners that can be

used to acquire image data on pulp fibers. 2 The most direct method

involves the use, of a scanner looking directly through a microscope.

The scanner can be either a mechanical one which directs the whole

image, via galvanometer mirrors, to an aperture through which a photomultiplier views a small segment of the field, (a pixel), or a light

source can be directed through the microscope optics to illuminate an individual pixel in the specimen which is then sensed by a suitably

placed photodetector.

A less direct method of image scanning, which we have used for the

experiments reported here, is to scan a photomicrograph of the specimen.

The photomicrograph can be scanned through a microscope just as the

original specimen, or it can be placed on a rotating drum type of scanner which scans successive pixels in a helix as the drum rotates.

We have used such a drum scanner to scan photomicrographic transparencies of pulp preparations. The scanner resolves a photograph into

pixels 25x10 6 meters square. Again, the resolution in terms of the

fiber depends on the magnification of the microscope and the photographic process. For each pixel, the optical density is measured to 8

bits (256 parts) over the range from 0.0 Optical Density to 2.0 Optical

Density. Here too, these densitometric measurements must be interpreted

to include the photographic process and the characteristics of the

microscope.

Once such a fiber image has been scanned, it is useful to be able

to view the scanned data before further processing. We have written a display program that gives a view of the scanned data useful for

discovering any gross errors that may have occurred in the scanning

process. This display represents the original 256 levels of scanned

density with 10 distinguishable symbols of varying darkness on a cathode

ray tube display. Various mappings can be used to map the actual range

of densities (typically smaller than the full potential range of 256

levels) into the 10 display symbols. Such a displayed scan is shown in

Fig. h. Actually, the full scan resolution is shown in the segment of a scanned image in Fig. 5, the image of Fig. U being obtained by displaying a suitable spatial average of the pixels of the original scan. 3

The possibilities of scanning fiber images are not limited to the

two types of data acquisition methods shown here. With direct microscope scanning, it is possible to change the focal plane of the microscope under computer control. With suitable narrow depth of field

optics, this makes possible the successive scanning of three dimensional

image arrays. Certain limited measurements that go beyond the measurements possible on two dimensional projections can thus be made. It is

necessary, however, to do fairly complex computer processing of the

multiple scans to separate the in- and out-of- focus portions of such

images if three dimensional reconstruction is to be done.

Another possibility for scanning fiber images with light microscopy

is to use monochromatic light as the illumination source. The spectral

6
Figure 4: Computer display of scanned fiber photomicrograph

showing, with 10 distinct characters, the range of

256 scan density levels

transmission and reflectance characteristic signatures of fibers thus

obtained can be used to obtain fiber classification. The widespread use

of this technology for viewing and analyzing satellite images of the

Earth suggests this false color viewing as a fruitful area for investigation.

Also, among the various scanning modalities, mention should be made

of the possibility (and indeed in some cases of the necessity) for

computer control of spatial selective scanning of fiber images. In

dilute preparations of fibers in suspension, a scanned image will

typically contain only a small fraction of the image area subtended by

fibers. To scan such an image at full resolution is to be wasteful of

time, storage capacity, and processing capability. When the scanner is

under direct computer control, it is possible to scan at low resolution

in order to find regions requiring higher resolution scanning, and then

to rescan these regions with the desired resolution. Thus one could

scan an image like that shown in Fig. h to locate the regions requiring

7
Figure 5: Computer display of full spatial resolution of scanned

photomicrograph

fine structural detail to be resolved. Then one could rescan to obtain

the resolution shown in Fig. 5» The first scan is, in effect, a map of

the regions subsequently to be scanned at higher resolution. Sometimes,

this rescanning is necessary not because of the need for higher resolution, but because analysis shows that further analysis of a previously

scanned region is necessary. Unless storage capacity for the very large

quantity of data scanned is available, this usually requires rescanning

of the region requiring further analysis.

Finally, there is a scanning modality closely related to a feature

available in existing commercial image analysis instruments. This is

the dynamic range selection capability. Once an image has been coarsely

scanned, it is possible to determine the actual density ranges occurring

in the image. Suitable readjustment of the scanning density range to

maximize contrast can then be done. This capability must be used

cautiously, however, because it is possible to introduce scanning

artifacts into an image at the boundaries where different density ranges

have been selected.

8
ALGORITHMS FOR ANALYSIS OF FIBER IMAGES

Once image data for a pulp preparation has been acquired, there are many kinds of measurements that may be made on the data. These measurements can be made with precisely defined, algorithmic procedures that

have the virtues of unambiguity and reproduceability . In the process of

constructing the algorithms for making these measurements, it becomes

evident that the level of precision and completeness required by a computer programming language demands that many decisions be made

regarding different interpretations of what would otherwise be con- sidered well defined measurement methods. To illustrate the varied ways

in which algorithmic definitions can be made, we will consider a set of

definitions of a property well known in fiber morphology studies to be

an important determinant of finished paper characteristics, the property

known as curl. These definitions will be illustrated with the semiautomatically obtained fiber data presented above.

We will not attempt to argue for any one definition of curl.

Rather, we will suggest that there are many more definitions, and

combinations of these, among which an intelligent choice may be made

regarding the one that incorporates notions of what morphological

properties are important in prediction of paper properties. Our objective is to illustrate the ease with which one can use a general purpose

computer to test such definitions and to produce candidates which may

then be further evaluated in the laboratory.

The data to be used in presenting each algorithm is shown in Fig.

3. For each traced point, the coordinates of that point scaled to the

original fiber image, are shown in micrometers. This has been done by

using a calibrated reticle in the microscope and photographic process

that was used to produce the images that were traced. Thus, relative to some arbitrary origin, the fiber traced in Fig. 3 begins at a point

whose coordinates, in micrometers, are (44,25), and then continues to

(31,111), (25, 164) , etc. The fiber can be closely approximated, for

fiber length calculation purposes, by assuming that these points are joined by straight line segments. Had the tracing points been much

farther separated, this would not be a good assumption. Using this

assumption, we can compute the distances between tracing points as shown

in Fig. 6. Each of the distances shown is rounded to the nearest

micrometer. The sum of these distances is 2022 micrometers. The actual

length calculated from the coordinate data is 2019 micrometers, the

disparity coming Yrom rounding errors.

We are now prepared to calculate a first proposed curl measure.

This measure corresponds to one discussed by Kibblewhite. 4 For a fiber, we calculate the length and divide this by the distance between end

points. If the fiber is relatively straight, this ratio will be near

1.0. If it curves very much, the ratio will be larger. In Fig. 7 we show several such ratios calculated for different segments of the same

fiber. For the initial segment, which is fairly straight, this curl

9
Figure 6: Distances between tracing points in single fiber

Figure J: Ratios of length along fiber segments to distances

between end points. Distances are calculated from

lower left fiber end

10
measure is 1.085 whereas for the longer segments possessing more curvature, the measure is 1.263, 1.8l4, and I .768 respectively.

We have displayed this curl measure for different segments in order

to suggest an idea to he developed further, namely, that certain measures might be assigned not to whole fibers but rather to segments of

fibers. These segments might possibly act like whole fibers in con- tributing structurally to the properties of the paper in which they are

used. It can be seen that with little additional difficulty the algorithm for defining this curl measure can be extended to be a continuously

varying function of position along a fiber rather than a simple function

of the whole fiber.

We obtain another proposed measure for curl by calculating the

angle of bend at each tracing point along a fiber. Since we are approximating the fiber by straight line segments, the angle between

these segments is a well defined quantity. In Fig. 8 we see the same fiber plotted with small circles marking the plotting points and with

the angle of bend at those points shown, in degrees. Thus, for the

example shown, there are pairs of segments between which the angle

varies in magnitude from 0 degrees to 69 degrees. The algebraic sign

denotes the direction of bend along the fiber, a bend to the right

Figure 8: Angle of bend (degrees) at each tracing point along

fiber

11
(going clockwise in the figure) being denoted, positively, and one to the

left negatively. At the end points where the angle is not defined, a zero is arbitrarily assigned.

One can imagine various ways of combining these angles. They can be averaged, their magnitudes can be averaged, the maximum value can be

used to characterize the whole fiber, or they can be used in more complex ways, including the possibility of combining the angle measure with other measures. We have performed such experiments and will report

one such combination involving angle and segment length below.

Yet another measure for curl can be obtained by generalizing the

angle measure slightly. If we take sequences of three tracing points,

they will, in general, uniquely define a circle passing through these

points. The exception occurs in the case when the points are collinear,

in which case the circle is degenerate, of infinite radius. In Fig. 9 we have plotted, at each tracing point, the radius in micrometers, of

the circle passing through that point and the two points on either side

of it. In the single case where three points are collinear, an 'S' for

'straight* is shown, as it is for the exceptional points at the ends of

the fiber.

Figure 9 : Radius (micrometers) of circle passing through each

tracing point and adjacent point on each side

12
This radius of curvature measure is slightly more general than the

simple angle measure because it allows information to be incorporated

regarding the distance between tracing points. In the special case where tracing points are equally spaced, points of equal angle will have

equal radius of curvature. For automatically scanned images this will

generally be the case. A complication enters, however, when equally

spaced tracing (or scanning) points straddle a sharp bend. It is

desirable, in that case, to use an additional scanning point at the

sharp bend, which introduces non-uniform spacing. In the manually

traced data shown here, these critical points have been deliberately

introduced. To see how the angle and radius of curvature measures are related, we have shown, in Table 1, the various angles of Fig. 8 in

increasing magnitude order, and for each one have given the corresponding radius of curvature at that point. Note that the radius of curva- ture does not necessarily decrease with increasingly sharp angle of

bend.

1 436 1 428 6

2 . 234 1 882 4

2 . 298 1 880 .

9

2 . 771 1710.3

5 . 702 819.7

6 . 632 458 . 1 6 808 600 .

7 . 943 432 .

3

8 . 271 569 .

6

1 1 .. 1 35 358 .

3

1 3 762 209 .

6

1 4 909 263.9

1 6 . 695 296.6

1 6 . 877 228 .

1 7 . 823 206 .

1 8 . 084 201.2

1 8 . 1 66 188.9

21 . 500 149.6

23 . 906 168.9

25 . 248 145.7

27 . 251 158.4

28 855 129.1

34 . 508 74 .

6

46. 333 54.0

53 . 1 30 63 .

6

58 . 012 148.5

68. 787 134.6

Table 1: Angle of bend (degrees) (left col.) as a function

of radius of curvature (micrometers) (right col.) of

the tracing points in Figures 8 & 9

13
We have suggested that the ease of defining algorithms on a general

purpose research computer enables one to experiment with new, complex

measurement procedures. One such algorithm enables us to incorporate

information on fiber length and curl in a single measure. To see how

this may be done, consider the diagram of Fig. 10. Here we see the same fiber, but with lengths indicated between various plotting points. The

points chosen are just those at which the angle of bend exceeds, in

magnitude, some prescribed amount, 10 degrees in this case. It is as though the fiber was considered to have been 'broken' at those points.

If we change the 'breaking' criteria to, say, 20 degrees, we get the set

of segment lengths shown in Fig. 11. Note that the segments become

longer in some cases because some of the bends of Fig. 10 may exceed 10

degrees but not the 20 degrees necessary for segmentation in Fig. 11.

If we segment at 30 degrees, we get the six comparatively long segments

shown in Fig. 12. In the extreme case of segmentation at 180 degrees,

there is no 'breaking' at all and we get the ordinary fiber length.

Figure 10: Segment lengths between bends sharper than 10 degrees

14
Figure 11: Segment lengths between bends sharper than 20 degrees

Figure 12: Segment lengths between bends sharper than 30 degrees

15
We have performed an extensive set of measurements, first reported

in Ref. 3, using this last notion of curl. A set of three pulps were analyzed from semiautomatically traced photomicrographs. The images

analyzed contained 2585 distinct fibers. They were drawn from three

pulps, a northern and a southern softwood, and a hardwood. Fig. 1 is a typical image from the set of southern softwood images. It contains 28

of the 2585 fibers analyzed.' For these 28 fibers, there were 317

tracing points.

In Fig. 13 we show the result of calculating a classical fiber

length distribution for these three pulps. We find, as expected,

that the hardwood fibers are primarily short, and the softwood fibers

longer. The distribution of fiber lengths is also broader for the

softwoods than for the hardwoods.

Length

Figure 13: Histogram of number of fibers in length classes of

0.1 mm. for three different pulp sources

16
Segment length (0.1 mm classes)

Figure ik: Histogram of number of fiber segments as a function of

segment length for curl angles from 180 degrees (front)

to 0 degrees (rear). Data from 620 Northern Softwood

fibers

E

e€

**-O

h.®

.0E

3z

Segment Length (0.1 mm classes)

Figure 15: Histogram of number of fiber segments as a function of

segment length for curl angles from l80 degrees (front)

to 0 degrees (rear). Data from 425 Southern Softwood

fibers

IT
!

Figure l6:

Segment length (0.1 mm classes)

Histogram of number of fiber segments as a function of

segment length for curl angles from 180 degrees (front)

to 0 degrees (rear). Data from 15^0 Hardwood fibers

A more interesting and informative way to view these data is shown

in Figures l4, 15, and l6. In Fig. lU, data for the northern softwood

•s given. He^e wi see a three dimensional surface which depicts information both on the fiber length distribution and on the curl distribution. The front of the surface can be seen to be the curve of the

northern softwood fiber length distribution as given in ig. * ever, the successive planes toward the rear of this figure show the

distribution of segment lengths for various angles a w ic e may be considered to have been 'broken' as discussed above Thus the

front plane has, for 180 degrees, the case of no breaking of the * Each successive plane shows the result of a three degree c a g

breaking criterion. In the rear plane, all bends of any angle great

than 0 degrees result in segments or breaks occurring, hence there are a large number of short segments obtained. Between the front and r olanes is found a great deal of information on both the length distri

Hi lTe cur! distribution (in the last of the senses dtscussed

above )

.

We can compare the three dimensional surface for the two softwoods

and the hardwood. We see distinctions and similarities in the two

softwoods, but a very significant difference between them and the

hardwood surface of Fig. l6. One interpretation of Fig. 16 is that

shows a,set of short, 'stiff fibers. They are stiff

J£eea those planes in front of that plane corresponding to about 45 degrees

there is virtually no change in the length distribution.

18
Other interpretations of these surfaces as descriptions of the

morphology of these three pulps are possible, as are other definitions

of the various morphological properties. Our purpose here is, however,

to illustrate the range of definitions possible and to suggest by

example, the ease with which these definitions can be modified and

combined to yield insight into measurement methods for characterizing

fiber morphology.

PATTERN RECOGNITION ALGORITHMS

The measurements described in the last section made critical (and we hope effective) use of the semiautomatic tracing data for fibers that we obtained with computer graphics tools operating on photomicrographs

of pulps. Although these measurements illustrate the possibilities of

algorithmic based measures, they do not yet demonstrate how these data

may be obtained automatically and how the algorithm derived resulting

automatic measurements may be obtained. For this, it is necessary to use automatic scanning methods and Pattern Recognition. We shall

demonstrate here how such automatically scanned fiber images as that of

Fig. 4 may be analyzed. It will also be necessary to show how the basic

pattern recognition step performed by the human operator in selecting

and separating distinct fibers in a fiber image may be accomplished by a computer.

We have shown, above, how a rectangular array of optical densities

representing the fiber image in Fig. 4 can be obtained. A common method

for extracting a pattern from such an automatic scan is to use density

thresholding. Typically, a density value, suitably chosen from the

range of densities that occur in the image, is used to distinguish all

pixels with densities below that threshold from those at or above the

threshold. This yields a binary image containing zeros for the points

below threshold and ones for the remainder. From this binary image, it

is possible, by means of several different algorithms, to find sets of

connected binary ones. Two ones are connected if they are adjacent in

the horizontal vertical, or diagonal direction. All the binary ones connected to some initial one or to those connected in turn to them,

recursively, are treated as a single connected object, or blob. A blob

is, then, a candidate for consideration as a fiber.

We see some of the problems that occur with this method in Fig. 17.

Here we have chosen, for a threshold, the mean value of the densities

that occur in the scanned image, namely 157* Then choosing the first

pixel with density not less than the threshold we extract the blob

containing that pi*el. What we notice in Fig. 17 is that the blob

thereby extracted contains three fibers. Two of these fibers are overlapping, and the remaining one is connected by a region in the image

containing some dark parts that are over the threshold, thereby con- necting two otherwise disjoint blobs. A more judicious choice of

threshold can solve the problem of spuriously connected objects. In

Fig. 18 we see the effect of merely raising the threshold by one density

level to 158. Here the long fiber becomes separated from the two

19
Figure IT: Boundary points of single connected blob extracted

from Figure h by thresholding at mean density, 157

Figure 18: Boundary points of single connected blob extracted

from Fig. 4 by thresholding at slightly higher density,

158

20
crossed fibers. Thus selecting a point of the crossed fibers no longer

causes the spurious fiber to be included. There still remains the

problem of separating the crossed fibers. We will not treat this problem

here, but defer it to a later study.

Another problem in automatic measurement and recognition of scanned

images is seen in the three successive thresholdings of Figures 17, l8,

and 19. Using thresholds of the mean, mean + 1, and mean + one standard

deviation we get different extracted connected blobs. What we actually

display in these figures is not the blobs themselves, but rather the

results of a boundary tracing algorithm.

Once a blob has been extracted and exists as an image of l’s and

0's, it is possible to choose a single binary pixel on the boundary of

the blob. This is a pixel which is a 1 and is adjacent to a 0 , unlike the interior pixels, all of which are surrounded by other ones. Starting with this pixel, it is possible to trace successively from one boundary pixel to another, going completely around the blob until the

Figure 19: Boundary points of single connected blob extracted

from Fig. 1+ by thresholding at mean density plus 1.0

std. dev. , l6b

21
original pixel is encountered. The sequence of such automatically

traced pixels is what we display in the three figures. Thus the

boundary sequence of pixels is somewhat analogous to the set of tracing

points obtained in the semi-automatic data acquisition case. Actually,

it is necessary to obtain a set of points in the interior of the fibers

to be strictly analogous to the semi-automatic case. We have connected

this boundary sequence in the order that the boundary pixels are en- countered to produce Figures 17, 18, and 19.

It is possible to make some useful measurements on these boundary

sequences, and on the blobs from which they are obtained. First, by

counting the pixels in the blobs, we can measure the area of the fibers

as they are projected on the focal plane of the microscope. Since the

images are not yet properly segmented, these areas are not meaningful as measures of individual fibers. The areas thus determined for the three

thresholds of 157, 158, and l64 are, respectively, 65691, 26o4l, and

15268 pixels. Since these measurements are not calibrated in dimensional units, they should be viewed as only relative area measurements.

We do note, as expected, that with increased threshold, the area of the

objects measured decreases.

Another useful measure is the perimeter, or boundary length of the

blobs. This is obtained by measuring the distance from one boundary

pixel to the next and summing all these distances. These distances will

all be proportional either to unity or the square root of two, corre- sponding to pixels that are adjacent vertically and horizontally or diagonally. In terms of the same relative units used in measuring the

areas, we get boundary lengths of 5272, 2477, and 2272. The number of

boundary points that contribute to these lengths are 500, 239, and 217

respectively. These results are summarized in the following table:

Threshold 157 158 164

Number of Boundary Points 500 239 217

Boundary Length , P 5272 2477 2272

Area, A 65691 26o4l 15268

p

2

/a 423.1 235-6 338.1

The last measure shown in the

of roundness of blobs, having

table is a minimuma commonly

value of

used measure of degree

4 Pi for a circular

blob and larger values corresponding to blobs like the present ones which are long and thin.

The simple pattern recognition algorithms demonstrated here can be

greatly improved. As with the analysis of semi-automatic ally obtained

data, the automatic scan data can be analyzed with algorithms that

perform significantly different operations on the data to produce

measurements that are superficially similar. An example, occurring

22
above, is the measurement of boundary or perimeter length. Two different measures can be seen to give perimeter lengths. One is the count

of the number of boundary pixels. Another is the length of the set of

straight line segments joining boundary points. Similarly with area

calculations, the count of interior pixels can give results different

from the area included in the sequence of straight line segments constituting one of the above perimeter definitions. These definitions must

be compared and tested in order to choose the most useful one for the

kind of measurement desired. As with the fiber separation problem

mentioned above, these pattern recognition algorithms will be the result

of a future study.

CONCLUSION

We have attempted to demonstrate how images of pulp fibers can be

measured in a variety of ways. No one of these ways is claimed to be

superior to another, but we do claim that the different measurements are sufficiently different that a basis exists for choosing among them,

given that criteria exist relating to how the measurements are to be

used. Since all these measurements are based on algorithms written for

large general purpose computers, they can not only be reproduced and the

results duplicated, but they can also be improved in a special sense.

In this sense, the improvement is in efficiency of performance of the

measurement algorithms without changing the functional nature of their

behavior.

When a new technology like image processing and pattern recognition

is introduced to the pulp and paper field, it is important that exploratory

investigations be permitted. It is also important that the results of

this exploration be directly useable once assurance is available that

the nature of the measurements to be made is well understood. The use

of algorithms as the facilitator of this conversion from exploration to

production has been demonstrated here. We hope that the subsequent

stages in this process will be carried on by our colleagues in the pulp

and paper industry and the electronics industry.

ACKNOWLEDGEMENTS

We would like to thank our colleagues E. L. Graminski for preparing

the fiber image data used in these experiments and, in general, for

suggesting more useful measurements to be made than even the powerful

tools reported here can accommodate; D. J. Orser who demonstrated many

times that algorithms that are elegant and carefully written also are economical and work; D. E. Rabin and D. A. Miller who helped get em- pirical data from one computer to another and from the computer into a form that can be understood by lesser beings like the author; and Mrs.

S. Lightbody who, by preparing the manuscript has made it possible for

the rest of the world to determine whether what we say makes any sense.

23
REFERENCES

1. Kirsch, R. A. et al, Experiments in Processing Pictorial

Information with Digital Computers, Proc. 1957 Eastern Joint

Computer Conf . , Assn, for Computing Machinery, pp. 221-229 (1957)-

2. Electronic Industries Assn., Computer Image Processing Equipment

Buyer's Guide (1977)> EIA Government Div. , 2001 Eye St., N.W.

Washington, D.C., 20006

3. Graminski, E. L. and R. A. Kirsch, Image Analysis in Paper Manufacturing, IEEE Computer Society Conf. on Pattern Recognition and

Image Processing (June 6-8, 1977)*

4. Kibblewhite , R. P. , Structural Modifications to Pulp Fibers:

Definitions and Role in Papermaking, TAPPI 60_ (l0):l4l (1977)*

24
NUS-114A IrtEV. 7-73)

U.S. DE.PT. of comm. BIBLIOGRAPHIC DATA

SHEET

1. PUBLICATION OR REPORT NO. NBSIR 78-II4 I42

2. Gov’t Accession

No.

3. Recipient’s Accession No.

4. TITLE AND SUBTITLE

Algorithms For Image Analysis of

Wood Pulp Fibers

5. Publication Date January 1978

6. Performing Organization Code

7. AUTHOR(S)

Russell A. Kirsch

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

10. Project/Task/Work Unit No.

11. Contract/Grant No.

12. Sponsoring Organization Name and Complete Address (Street, City, State, ZIP)

same as item 9

13. Type of Report & Period Covered

final

14. Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

16. ABSTRACT (A 200-word or Jess (actual summary ot most significant information. If document includes a significant

bibliography or literature survey, mention it here.)

Image analysis technology can he used to measure the visible

morphology of pulp fibers. But before such measurements can be accepted, it is necessary to achieve precise definition of the necessary

measurements in the form of suitable algorithms that have been experimentally tested on images of actual, fiber data. We present such

measurement results on both semiautomatically traced fiber data and on automatically scanned images. We explore the variety of definitions

possible for some simple veil knovn properties of wood fiber morphology

by applying suitable algorithms to fiber image data. Finally, ve

suggest that this exploratory approach to the specification of the

precise image analysis measurements needed in paper manufacturing can facilitate the introduction of a technology for process control that

vill result in savings in paper manufacturing cost and in reduction of

energy requirements.

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name; separated by semicolons)

Algorithms; artificial intelligence; image analysis;

morphological analysis; paper fibers; pattern recognition; pulp

j characterization.

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