Image Processing in the Biomedical Sciences

COMPUTERS AND BIOMEDICAL RESEARCH 1, 112-123 (1967)

Image Processing in the Biomedical Sciences

GEORGE N. EAVES*

Grants Associates Program, Division of Research Grants, National Institutes of Health, Bethesda, Maryland 20014

Received July 1, 1966

A survey of current research and development activities in the automated analysis of biomedical images discusses the application of computer techniques to the extraction of information from photographs. x-ray films, and images on microscope slides. Accomplishments in the primary areas of endeavor include the analysis of chromosomes, the differentiation of white blood cells, the interpretation of radiographic images, and other areas of investigation. The limitations and problems encountered in the biomedical application of pattern recognition techniques are also discussed. Existing and potential value of utilizing automated image processing in biomedical investigations and medical practice are considered in terms of increasing analytic capability as well as simply quantifying biomedical data which occurs in visual form.

INTRODUCTION

Only within the last five years has the digital computer been exploited as a research tool by the biomedical scientist. Within this interval of time, the expan- sion of its use and the scope of its application have been impressive. Paralleling the evolutionary pattern of computer use in the other areas of science, early biomedical applications were restricted to computation involving data which were in alphanumeric form. Data processing was then applied to non-numerical data with the concomitant development of techniques for the reduction and analysis of analog signals, such as in the analysis and interpretation of electro- cardiogram and electroencephalogram signals. Only recently have attempts been made to apply computational techniques to the more complex structures of composite images.

During the last few years, the health sciences have increasingly demonstrated interest in the potentialities for applying computer techniques to the extraction of information from images on microscope slides, photographs of slides, x-ray film, autoradiograms, and photographs of cultures of microorganisms. In each

* Presently, Health Scientist Administrator, Special Research Resources Branch, Divi- sion of Research Facilities and Resources, National Institutes of Health.

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instance, the task the computer was called upon to perform was time-consuming and tedious from the standpoint of the investigator or the technician. As a consequence, image processing has often been viewed as simply a mechanism for reducing the tedium of biomedical investigations and clinical analyses. Of significant importance to science, however, is the hope that data reduction and analysis techniques used in conjunction with digital representation of images may permit an increase in the effective resolution of biomedical observation. Depending upon the extent to which extraneous information or noise can be systematically removed from the field of view, there is the possibility that ob- jects or relationships can be revealed with greater clarity. It is disquieting to consider the relative difficulty of quantifying that large domain of biomedical data which occurs in visual form. The ability to assign a metric parameter to visual data, even in the absence of precise definition of the appropriate unit of information, seems to presage the emancipation of certain areas of biomedical science from an exclusive dependence on subjective evaluation.

Unfortunately, too little has been done in the biomedical sciences to date to establish definitively the utility of automated image analysis. Moreover. success in the analysis of patterns and images has been slow in other areas of science as well, especially those which are associated with our national defense estab- lishment. Developments in the latter would not appear to be impeded by either a lack of interest or money. It is not possible then to predict certain success in the area of biomedical image analysis. Furthermore. any large-scale investi- gative effort in this area will undoubtedly be quite costly, if for no other reason than that of equipment cost. It is concluded, however, that intensified efforts in biomedical image processing hold great promise and that the risks involved are worth taking.

ACCOMPLISHMENTS AND CURRENT ACTIVITIES

Accomplishments and activities in biomedical image processing have been limited to the efforts of a small number of individuals working on a limited variety of image types. The effort is currently concentrated in three major areas: analysis of chromosomes, differentiation and classification of leukocytes, and reading of radiographic images. Of these, the primary efforts are in the development of computer-oriented analysis of human chromosomes. The rea- sons for this are related to the prohibitively time-consuming nature of the technique for karyotyping a metaphase spread, the probability of increasing the inherent diagnostic potentiality by removing the limitations imposed by human perceptual capabilities, and the ever-increasing importance of chromosome analysis as an essential tool in both basic research and medical diagnosis.

It has been demonstrated that consistent chromosomal abnormalities occur in association with certain congenital disorders such as the Down, Turner and

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Klinefelter syndromes, following exposure to irradiation, and in association with tumors induced by both viral and chemical mutagens. Less is known about chromosomal abnormalities or variations in spontaneously arising malignancies. At the present time, the manual process of analyzing the chromosomes of white cells grown in tissue culture requires a minimum of three days from the withdrawal of blood to the end of karyotyping. At this rate, it has been esti- mated that a senior scientist and a highly trained technician can perform the chromosomal analyses of only about 100 individuals in one year! Thus, it is encouraging to tind that several laboratories have published reports on automatic karyotyping of carefully selected metaphase spreads.

Much of the foundation for the application of computers to the automatic classification of chromosomes was developed by Butler et al.lJ, who used the CHLOE film-scanning machine to convert the essential information in photographs of chromosome metaphase spreads to digital form. In this technique, the individual chromosomes are recognized by the computer programs and each one is subsequently characterized by a set of seven numbers called Euclidean moment invariants, which are independent of both the position and the orienta- tion of the chromosome on the picture. Each chromosome is thereby represented as a point in a seven-dimensional measurement space and a measure is then defined in such a way that chromosomes with nearby representative points tend to be similar in shape. The computer then progressively pairs the closest matched (homologous) chromosomes. The measurement and shape-construction data are processed from the numerical information that results in a recon- structed printout metaphase picture of the chromosomes with a line segment drawn between each homologous pair. Since the computer program pairs each chromosome with its corresponding mate on the basis of shape similarity, an aberrant chromosome, e.g., with deletions or reciprocal translocations, cannot be paired because there is no corresponding chromosome. Such anomalies are sorted ‘out and classified as aberrant chromosomes. Thus, chromosomes with minute changes that involve deletions or translocations which are not always detectable by eye may be reliably detected by such a rapid automatic and sensitive scanning device as CHLOE. 3 At the present time, a photograph of a preselected metaphase spread with a full complement of chromosomes, ran- domly arrayed, can be scanned with this system in less than two seconds. The digital information can be stored on magnetic tape and processed in less than two minutes for each chromosome complement.

Ledley and Ruddle4 have devised a computer regime for the analysis of chromosomes in which a series of photomicrographs of selected metaphase spreads is read directly into the memory unit of a computer by a scanning device called FIDAC (Film Input to Digital Automatic Computer). Homologous chromosomes are paired by criteria of overall length, of arm-length ratio, and

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of area. Each pair is subsequently placed into one of seven groups or classes according to the standardized idiogram sequence. A count of the chromosomes is also made, the total chromosome length for the frame is evaluated, and the fraction each chromosome contributes to this length is determined. This system for examination of chromosomes by syntax-directed recognition requires only about half a second for analyzing and classifying each chromosome, or about 20 seconds for the full complement of human chromosomes. The results of each step in the analytic process can be printed out by the computer; the final printout consists of a schematic idiogram of the complement of chromosomes being analyzed.

Using chromosome size as the only diagnostic parameter, Neurath et (11.” have developed an automated system for human chromosome analysis which is capable of scanning from photographs or directly through a microscope. The photographic print or image formed through a microscope is scanned by a slow- scan TV camera system, the output from which is converted into a digital record on magnetic tape by analog-to-digital converter. Since the resulting initial digital tape contains considerable unnecessary information, it is reprocessed as the first step in the digital computer program. In programming, the chromosomes are represented by a contour line of equal grey level; however, only in a few spreads can all of the individual chromosomes be represented satisfactorily by this criterion. Hence, instead of tape recording the analog-to-digital converter output from the initial viewing, a digital limit is selected and the result is viewed on the TV monitor. This visual check permits the selection of a nearly optimum grey level at which the equal grey contour lines give a fair representation of the chromosomes in the spread. Thus, spreads suitable for processing by this method can be selected before recording the initial scanner output. The final printout from this automated technique presents chromosome outlines erect, numbered, and arranged similarly to the standard karyotype, with the relative chromosome area and arm-length ratio indicated.

The feasibility of automatically selecting metaphase spreads for analysis is being investigated by Wald et al .6 The evolving automatic cytogenetic analysis system would detect mitotic cells, place them under microscopic observation, and focus the microscope. The mitotic cells would then be classified into two categories: those suitable for chromosome count only and those suitable for chromosome count and karyotyping. The suitable metaphase spreads are then scanned by an ultra-precision electronic flying spot scanner and/or photographed. The information from the slide or, currently and alternatively, from photographs is converted to digital form and recorded on magnetic tape for computer analysis. A pilot program based on two parameters-area and perim- eter-has been used to karyotype the selected metaphase cells.

The investigations just discussed have exploited the morphological criteria of

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size and shape of chromosomes as the primary basis for classification-in much the same way as the manual karyotype is produced. In these approaches the chromosome is treated either in black and white or, at most, eight grey levels. Mendelsohn et al.? have contributed an additional and significant advancement by emphasizing the content and distribution of a chemical constituent of chromosomes as the essential parameter of automated chromosome analysis. On the basis of the intrinsic variability of size and shape of chromosomes resulting from the effects of differential condensation and preparative distortion, deoxyribonucleic acid was selected as a parameter with the greatest potential in terms of genetic theory and the availability of suitable cytochemical methods. In these investigations, optical density measurements of gallocyanin-chrome alum stained chromosomes were obtained automatically in digital form by CYDAC (CYtophotometric DAta Converter) ? a scanning digital cytophotometer. A partly automated computer analysis of the data then provided the relative stain content of individual chromosomes and chromosome arms. The results of these investigations indicated that gallocyanin-chrome-alum stain content of individual chromosome arms correlates closely with length and ultraviolet ab- sorbance values reported in the literature. Thus, it appears that this approach has promising applications in the automation of karyotype analysis as well as in cytogenetic studies.

The peripheral blood smear is another valuable diagnostic and research tool with imposing potentiality. Unfortunately, the vast amount of information that is present in routinely prepared blood films cannot be fully utilized at present because of the limitations imposed by classical technology. It is known, for example, that identifying parameters such as changes in cell size and morphology are associated with aging, polyploidy, malignancies, thrombotic throm- bocytopenic purpura, megaloblastic anemia, iron deficiency, and radiation exposure. Determining rates of occurrence of certain diagnostically important rare cell types is at present a formidable statistical problem. In addition, the ability to identify slight, but highly indicative, changes in size or morphology of commonly occurring cells represents a primary obstacle to exploitation of the information available in a blood smear. Ingram and Preston” are currently engaged in the construction and testing of a fully automatic instrument that would classify blood cells on the basis of their topological characteristics. Investigations directed initially toward the automated differentiation of leukocytes have incorporated the CELLSCAN system, which recognizes cells by topo- graphical analysis of the black and white computer-stored image of the cell magnified by means of conventional microscope optics. The basic operation is sizing and counting individual morphological components of each cell. Each field examined is divided into elementary units or bits and the computer initially stores the entire black and white (binary) image in its tape memory.


It then applies the “shrink” technique to the stored image, examining the individual discrete black areas in the entire field and subtracting from them those bits that are peripheral but otherwise contiguous with similar bits on all sides. Sequential scans eventually reduce each discrete black area to a single bit which is counted as a single dense unit. A monochrometer has been incorporated into the system so that tinctorial characteristics of cells can be utilized in the recognition process, even though the images processed are black and white. The binary image of certain cell types changes with change in wave- length of illumination. Preliminary studies of unstained cells using phase- contrast optics are also in progress. In contrast to this approach, Prewitt and MendelsohnQ derived the identifying parameters for leukocyte recognition exclusively from the optical density frequency distribution without exploiting obvious topological features such as nuclear shape and number of nuclear lobes.

Development in the area of automated reading of radiographic images has been concerned primarily with mammograms and chest x-rays. Mammography is now established as useful in the diagnosis of palpable lesions and in discov- ering disease not evident by palpation. If applied on a wide-enough scale, radiograms of the breast may be of value in the early detection of breast cancer. Thus, in order to circumvent the problems inherent in routine viewing of the large numbers of radiograms of presumably asymptomatic patients, WinsberglO has proposed the automation of mammogram reading by means of optical scanning and computer interpretation. Probability of success in this endeavor is enhanced by the compatibility of mammograms to quantitative analysis because of the inherent symmetry of the two breasts and the importance of density clusters rather than pattern information in the formula- tion of diagnosis. The exact pattern produced by a tumor is not described mathematically; rather, redundant patterns representative of normal tissue are discarded and the anomalous, but never explicity defined, patterns of disease are extracted from the remainder. In addition to the progress in automating mammogram reading, a practicable and useful diagnostic index derived from chest x-rays has already been automated. Becker et aZ.ll and Meyers et a1.l” have successfully determined the cardiothoracic ratio, a standard diagnostic index employed by physicians for detection of cardiac pathology, directly from unaltered x-ray film through the use of a digital computer.

There is currently an effort to develop completely automatic techniques for the high-speed computer analysis of the three-dimensional structures of neurons. Utilizing Golgi-Cox preparations, neuroanatomists have manually and laboriously traced individual dendrite branches in three dimensions for a small number of cortical neurons, and have attempted to characterize the branching structure of the dendrite tree of such nerve cells. This technique incorporates

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successive sections of a cortex photographed through the microscope as the plane of focus of the microscope is moved through the specimen in measured steps. Thus, the three-dimensional structural information desired is contained in a sequence of photographs. Ledley l3 has utilized the FIDAC instrument to read such serial sections into the computer’s memory for automatic analysis. In addition to distinguishing dendrites from blood vessels, the computer program is designed to describe the parameters of relative size of the cell body; the number of dendritic segments of first, second, and higher orders; the re- spective lengths of the dendritic segments; and, the overall degree of waviness of such segments.

Glaser and Wattenburg I4 have constructed an automated system for the growth and analysis of large numbers of bacterial colonies using an environmental chamber and a computer-controlled flying-spot scanner. This system has the capability of counting and analyzing large numbers of colonies of bac- teria and other microorganisms and identifying the organisms by observations of colony morphology, growth rate, nutritional requirements, drug resistances and other characteristics observable during growth on solid media and in a controlled environment. Properly tabulated and interpreted results of such large scale observations can be used potentially to select and characterize mutants, to construct genetic maps of microorganisms, to investigate taxonomic relationships among groups of organisms, to study mutational rates at various sites, and possibly, to identify and assay organisms for medical dia@osis and public health purposes.

A unique application of image processing techniques is exemplified by the preliminary investigations of Sokal and Rohlfl” on the feasibility of random scanning of taxonomic characters. Recognizing the difficulty in manually meas- uring and recording taxonomic characters at speeds and in quantities com- mensurate with the ability of modern computers to process the data, these investigators have proposed to record agreement in visible structures over ran- domly selected minute areas of images of pairs of organisms. This procedure would be amenable to optical scanning of photographs, films, microscope slides, or drawings. In terms of phenetic relationships, preliminary investigations have demonstrated that the random scanning method does yield meaning- ful classifications.

In related research, the Cognitive Information Processing Group at the Massachusetts Institute of Technology, under the direction of Eden and Ma- son,1° have completed a first phase in the development of an automated system which would provide the blind person with an auditory representation of language. In this system, strips of printed text cut from a book page are read by an opaque low-resolution scanner. Local logical operations culminate in a contour scan of each text letter. The central computer then determines a


code word based on this contour and identifies the letter by searching for the code word in a letter dictionary. The letter identification routine selects a stretch of prerecorded speech sound for each letter and this sound is subsequently transmitted through an audio generator so that the blind subject hears spelled speech. Recent advances in phonology and speech communication are being utilized in current investigations directed towards the development of an output which resembles natural English speech.

LIMITATIONS AND PROBLEMS

While the foregoing report of activities in the area of computer-oriented analysis of biomedical images presents an encouraging array of accomplishments, this area of investigation and development is not without serious problems that produce crippling limitations on both complete automation of a procedure and its practical applications. For example, one of the most tedious and time-consuming tasks in preparing karyotypes either manually or automatically is that of manually locating metaphase spreads suitable for analysis. Depending upon the preparation, this initial scanning may require from one hour to two days, for the automated analysis of a metaphase spread currently requires that all of the chromosomes be without touching, overlapping, or twist- ing distortions. Even when a program is developed which can select suitable metaphase spreads from the stained preparations of in vitro cultured cells- and there is current activity in this area-it may impose statistical limitations on the analysis of an entire clone of cells, for the number of suitable cells varies considerably from preparation to preparation. Hence, the obvious solution to obtaining the greatest amount of information from a given preparation involves the development of “software” appropriate to the analysis of most cells on a slide rather than of a select few. Similarly, the high-speed computer analysis of three-dimensional structures of neurons are all concerned with recognition. For example, there is difficulty in computer recognition of an object as either a cell body or a part of a dendrite, in the recognition of continuity of an object from frame to frame, and in the recognition of branch points or end points.

Equally formidable are the inherent problems of distortion and condensation of cells and their structural subunits during preparation, staining, and fix- ing. Inseparably related are the qualtitative and quantitative variations in staining reactions of biological specimens. Mammalian chromosomes, for example, have dimensions which approach the limit of optical resolution and have highly organized structures with very heterogeneous distributions of chromophore. As such, chromosomes are not easily measured photometrically with visible light. Furthermore, if subunits of the chromosomes are to be

utilized as parameters for analysis and differentiation it will be necessary that highly specific stoichiometric staining techniques be developed, especially since highly refined scanning instruments hold promise for the ultimate detection of chromosomal substructures, such as chromomeres, banding patterns and gene loci. It therefore seems apparent that more sophisticated histological techniques involving reproducibility, specificity and stoichiometry are critical to the continued progress in automatic biomedical image processing.

It must be emphasized that obstacles to further progress are related also to the difficulty in defining appropriate and adequate criteria which could be combined into effective descision schemes for recognizing, interpreting, and differentiating both normal and abnormal biological materials. In many of these techniques, the use of linear and orthogonal partitions of the property space may not be appropriate discrimination procedures. Adequate classification. identification, and differentiation may well depend upon requirements for higher dimensional property spaces, partially redundant parameters, and non- orthogonal as well as nonlinear discrimination. Solution to these problems may well result from “software” or program-oriented studies such as those con- ducted by Lipkin et QE.,~~ whose approach is directed at the macro-level of patterns rather than the general present approach of quantification at the micro- level. Having an ultimate end of analysis bolstered b’y synthesis and description, these studies are based on the contention thlat the general-purpose computer does not limit the investigator to the sort of methodology characterized by immediate reduction of information, information reduction dependent upon peculiarities of the information sample, and treatment of information in purely numerical terms.

While the areas of current activity are of obvious importance, these are not necessarily the areas where automation is most urgent either medically or economically. It is predictable, however, that such efforts will generate a competence applicable to many other problems. Most of the investigations discussed have been directed toward the extraction of specific information from an image, with the concomitant exclusion of numerous potentially valuable characteristics. It is, of course, necessary to limit and simplify identifying parameters during the developmental stage; however, it is expected that “software” can be designed for simultaneously extracting multiple criteria. As a case in point, the automatic determination of a single diagnostic parameter from an x-ray film would not necessarily warrant incorporating that technique as a routine diagnostic procedure. Similarly, while the feasibility of automatically processing cytogenetic material is well established, much refinement and development must be done before such a technique can be employed on 3 routine basis.


The foregoing examples suggest at least two important considerations. First, developments in image processing must not be limited by exclusive concern with merely capturing and quantifying data, but must consider pattern recognition as well as other broader concepts of data analysis. Secondly, as a logical consequence of development, there must be criteria for evaluating progress in terms of utilization for service functions. The latter consideration is perhaps the most difficult, for here the primary obstacles are related to the problem of assessing progress without relative historical perspectives to guide utilitarian developments. Thus, until experience can guide, accomplishments must be measured in terms of geometric, interrelated progressions, rather than by ac- cumulated summations.

Utilization of the advances in image processing will then necessitate defining those areas wherein the need for automated processing is most critical. For example, there is a sizable expenditure of time and effort in the routine reading of Papanicolaou smears in searching for early cases of cancer of the uterus. A desirable screening program would involve periodic examinations of the entire high-risk segment of the population; however, such an activity would, at the present time, completely overwhelm available resources. There are many other possibilities for utilizing image processing in research activities currently impeded by the limitations of time, competence and human perceptual abili- ties. Among these are investigations of lymphocytic responses associated with the allergic reaction; mast cell changes during the immune process; sequences of events associated with tissue transplant rejection; microbial sequences and morphological changes during parasitemic infections, such as malaria; events associated with intracellular parasitic invasion, such as viral diseases, tubercu- losis, and leprosy; morphologic aspects of pharmacologic specificity; growth and development, including teratology; and, the initiation, development and progression of malignancies.

The evaluation of urgency and relative importance in defining areas of greatest need should incorporate criteria related to the ultimate value to medical practice and biomedical research, the need for amplification of existing diagnostic techniques and the development of new ones, the supplementation of critical manpower shortages, the contribution to maximum utilization of existing technical and medical competences, and cost-utility relationships. The ultimate decision of urgency based on such criteria will, in large part, fall upon clinicians gifted with the ability to survey the state-of-the-art in medicine and to project this trenchancy into predictions of present and future needs. Consequently, the clinician must provide direction, motivation, and stimula- tion as an integral and functioning member of the biomedical engineering team.

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CONCLUSION

Activity and accomplishment in the newly emerging science of biomedical image processing offer great promise for the future magnification and extension of applied as well as basic biomedical research. There is every indication that within the near future automation may eliminate some of the routine procedures and computation from the already limited schedules of scientists. permit the use of currently time-consuming or visually difficult techniques in routine testing of biological and other materials. and provide heretofore restricted, but vitally important, diagnostic techniques to a large number of individuals. Of imposing significance is the awesome potentiality for increasing visual perceptibility by utilizing the already highly advanced hardware technology exemplified by recent developments in scanners and computers. Thus, image processing in the biomedical sciences is providing still further motivation for the progressive interaction of biomedical science and engineering technology. As such, biomedical engineering will contribute yet another vital component to the progress of scientific research and its application to better and more extensive medical care.

ACKNOWLEDGMENT

The author wishes to express his appreciation to Mr. Lester Geiger for assistance in retrieving the background information used in this document.

REFERENCES

1. BUTLER, J. W., BUTLER, M. K., AND STROUD, A. Automatic classification of chromosomes, in “Data Acquisition and Processing in Biology and Medicine,” Vol. 3, (K. Enslein, Ed.) pp. 261-275 (proceedings of the 1963 Rochester Conference). Pergamon Press, New York, 1964.

2. BUTLER, .I. W., BUTLER, M. K., AND STROUD, A. Automatic classification of chromosomes -11, in “Data Acquisition and Processing in Biology and Medicine,” Vol. 4. (K. Enslein and J. F. Kinslow, Eds.), pp. 47-57 (proceedings of the 1964 Rochester Conference). Pergamon Press, New York, 1965.

3. BUTLER, J. W. (personal communication). 4. LEDLEY, R. S., AND RUDDLE, F. H. Automatic analysis of chromosome karyograms,

in “Mathematics and Computer Science in Biology and Medicine,” pp. 189-209 (proceedings of conference, Oxford, 1964). H. M. Stationery Office, London, 1965.

5. NEURATH, P. W., BABLOUZIAN, B. L., WARMS, T. H., SERBAGI, L. C., AND FALEK, A. Human chromosome analysis by computer-an optical pattern recognition problem. Ann. N.Y. Acad. Sci. 128, 1013-1028 (1966).

6. WALD, N., FEAGIN, F., AND RANSHAW, R. (personal communication). 7. MENDELSOHN, M. L., CONWAY, T. J., HUNGERFORD, D. A., KOLMAN, W. A., PERRY, B.

H., AND PREWITT, J. M. S. Computer-oriented analysis of human chromosomes-I. Photometric estimation of DNA content. Cytogenetics 5, 223-242 ( 1966).

8. INGRAM, M., AND PRESTON, K., JR. Importance of automatic pattern recognition techniques in the early detection of altered blood-cell production. Ann. N.Y. Acad. Sci. 113, 1066-1072 (1964).


9. PREWITT, J. M. S., AND MENDELSOHN, M. L. The analysis of cell images. Ann. N.Y. Acad. Sci. 128, 1035-1053 (1966).

10. WINSBERG, F. (personal communication). 11. BECKER, H. C., NE~LETON, W. J., JR., MEYERS, P. H., SWEENEY, J. W., AND NICE,

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14. GLASER, D. A., AND WATTENBURG, W. H. An automated system for the growth and analysis of large numbers of bacterial colonies using an environmental chamber and a computer-controlled flying-spot scanner. Ann. N.Y. Acad. Sci. 139, 243-257 (1966).

15. SOKAL, R. R., AND ROHLF, F. J. Random scanning of taxonomic characters. Nature 210, 461-462 (1966).

16. EDEN, M., AND MASON, S. J. (personal communication). 17. LIPKIN, L. E., WATT, W. C., AND KIRSCH, R. A. The analysis, synthesis, and description

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Image Processing in the Biomedical Sciences