Image Processing in Biological Science

This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1969.
This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived

• Language: English
• Publisher: University of California Press
• Release date: Mar 29, 2024
• ISBN: 9780520317529

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UCLA FORUM IN MEDICAL SCIENCES

VICTOR E. HALL, Editor

MARTHA BASCOPÉ-ESPADA, Assistant Editor

EDITORIAL BOARD

Forrest H. Adams

Mary A. B. Brazier Louise M. Darling Morton I. Grossman

William P. Longmire

H. W. Magoun

C. D. O’Malley Sidney Roberts Emil L. Smith Reidar F. Sognnaes

UNIVERSITY OF CALIFORNIA, LOS ANGELES

IMAGE PROCESSING IN BIOLOGICAL

SCIENCE

UCLA FORUM IN MEDICAL SCIENCES

NUMBER 9

IMAGE PROCESSING

IN BIOLOGICAL SCIENCE

Proceedings of a Conference held November, 1966

Sponsored by the UCLA School of Medicine, University of California,

Los Angeles, and the National Institutes of Health

WILFRID J. DIXON, MARY A. B. BRAZIER and BRUCE D. WAXMAN

CO-CHAIRMEN

DIANE M. RAMSEY

EDITOR

UNIVERSITY OF CALIFORNIA PRESS

BERKELEY AND LOS ANGELES

1968

University of California Press

Berkeley and Los Angeles, California

© 1969 by The Regents of the University of California

Library of Congress Catalog Card Number: 68-63727

Printed in the United States of America

PARTICIPANTS IN THE CONFERENCE

WILFRID J. DIXON, Co-Chairman

Department of Biomathematics, UCLA School of Medicine

University of California

Los Angeles, California

MARY A. B. BRAZIER, Co-Chairman

Brain Research Institute, UCLA School of Medicine

University of California

Los Angeles, California

BRUCE D. WAXMAN, Co-Chairman1 2

Special Research Resources Branch

Division of Research Facilities and Resources

National Institutes of Health

Bethesda, Maryland

DIANE M. RAMSEY, Editor]

Astropower Laboratory

Douglas Missile and Space Systems Division

Newport Beach, California

W. Ross ADEY

Space Biology Laboratory and Brain Research Institute

University of California

Los Angeles, California

STEPHEN L. ALDRICH

Research and Development

Central Intelligence Agency

Washington, D.C.

HARRY BLUM

Synthetic Coding Branch, Data Sciences Laboratory

Air Force Cambridge Research Laboratories

Bedford, Massachusetts

PATRICIA M. BRITT+

International Business Machines Corporation

Los Angeles, California

1 Present addresses:

2 National Center for Health Services Research and Development, Health Services and Mental Health Administration, National Institutes of Health, Bethesda, Maryland.

f Division of Research, Reiss-Davis Child Study Center. Los Angeles, California.

+ Health Sciences Computing Facility, University of California, Los Angeles, California.

DANIEL BROWN

Test and Electronic System Simulation Department

Space Technology Laboratory, Inc.

Redondo Beach, California

D. E. CLARK

Medical Computing Unit

University of Manchester

Manchester, England

WESLEY A. CLARK

Computer Systems Laboratory

Washington University

St. Louis, Missouri

GERALD COHEN

Biomedical Engineering Branch, Division of Research Service

National Institutes of Health

Bethesda, Maryland

GEORGE N. EAVES*

Special Research Resources Branch

Division of Research Facilities and Resources

National Institutes of Health

Bethesda, Maryland

MURRAY EDEN

Research Laboratory of Electronics

Massachusetts Institute of Technology

Cambridge, Massachusetts

FRANK ERVIN

Department of Psychiatry

Massachusetts General Hospital

Boston, Massachusetts

GERALD ESTRIN

Department of Engineering

University of California

Los Angeles, California

HELEN H. GEE

Computer Research Study Section, Division of Research Grants

National Institutes of Health

Bethesda, Maryland

% Molecular Biology Study Section, Division of Research Grants, National Institutes of Health, Bethesda, Maryland.

DONALD A. GLASER

Department of Molecular Biology and Virus Laboratory

University of California

Berkeley, California

LESTER GOODMAN

Biomedical Engineering and Instrumentation Branch

Division of Research Service

National Institutes of Health

Bethesda, Maryland

MARYLOU INGRAM

Department of Radiation Biology and Biophysics

The University of Rochester School of Medicine and Dentistry

Rochester, New York

RICHARD J. JOHNS

Sub-Department of Biomedical Engineering

The Johns Hopkins University School of Medicine

Baltimore, Maryland

R. DAVID JOSEPH

Astropower Laboratory

Douglas Missile and Space Systems Division

Newport Beach, California

BALDWIN G. LAMSON

Director of Hospitals and Clinics

University of California

Los Angeles, California

JOSHUA LEDERBERG

Department of Genetics and Kennedy Laboratories

Stanford University Medical School

Palo Alto, California

LEWIS LIPKIN

Perinatal Research Branch

National Institute of Neurological Diseases and Blindness

Bethesda, Maryland

JOSIAH MACY, JR?

Department of Physiology, Albert Einstein College of Medicine

Yeshiva University

Bronx, New York

Present address:

• Division of Biophysical Sciences, University of Alabama Medical Center, Birmingham, Alabama

BRUCE H. MCCORMICK

Department of Computer Science

University of Illinois

Urbana, Illinois

MORTIMER L. MENDELSOHN

Department of Radiology

Hospital of the University of Pennsylvania

Philadelphia, Pennsylvania

MARVIN MINSKY

Department of Electrical Engineering

Massachusetts Institute of Technology

Cambridge, Massachusetts

ROBERT NATHAN

Jet Propulsion Laboratory

California Institute of Technology

Pasadena, California

PETER W. NEURATH

New England Medical Center Hospitals

Boston, Massachusetts

NILS J. NILSSON

Artificial Intelligence Group, Applied Physics Laboratory

Stanford Research Institute

Menlo Park, California

AMOS NORMAN

Department of Radiology, UCLA School of Medicine

University of California

Los Angeles, California

SEYMOUR PAPERT

Department of Mathematics

Massachusetts Institute of Technology

Cambridge, Massachusetts

ARNOLD PRATT

Division of Computer Research and Technology

National Institutes of Health

Bethesda, Maryland

KENDALL PRESTON, JR.

Electro-Optical Division

The Perkin-Elmer Corporation

Norwalk, Connecticut

JUDITH M. S. PREWITT

Department of Radiology

Hospital of the University of Pennsylvania

Philadelphia, Pennsylvania

JEROME A. G. RUSSELL

Research Data Facility, The Institute of Medical Sciences

Presbyterian Medical Center

San Francisco, California

DENIS RUTOVITZ

Medical Research Council

Clinical Effects of Radiation Research Unit

London, England

GEORGE A. SACHER

Division of Biological and Medical Research

Argonne National Laboratory

Argonne, Illinois

ROBERT H. SELZER

Jet Propulsion Laboratory

California Institute of Technology

Pasadena, California

ROBERT R. SOKAL

Department of Entomology

University of Kansas

Lawrence, Kansas

EDWARD F. VASTÓLA

Division of Neurology, Department of Medicine

State University of New York

Downstate Medical Center

Brooklyn, New York

HERMAN W. VREENEGOOR

Division of Computer Research and Technology

National Institutes of Health

Bethesda, Maryland

NIEL WALD

Graduate School of Public Health

University of Pittsburgh

Pittsburgh, Pennsylvania

WILLIAM S. YAMAMOTO

Department of Physiology

University of Pennsylvania School of Medicine

Philadelphia, Pennsylvania

FOREWORD

BRUCE D. WAXMAN

Co-Chairman

First, let me express our gratitude to the UCLA staff who so willingly worked with us in the development of this conference on biological image processing. We are, of course, very much indebted to Drs. Dixon and Brazier, assisted so ably by Dr. Ramsey.

I should like to exercise an administrator’s prerogative to reflect upon the reasons of the National Institutes of Health in supporting this conference. Most of you are aware of current activities in biological image processing in this country and probably share with me the belief that (a) the efforts are comparatively small, (b) the equipment available to biomedical scientists for image processing is generally less than state-of-the-art, and (c) much of the current effort has resulted as spin-offs from work in other areas. The demands of the moment are for reasonable increases in the volume of this activity in the biomedical sciences and for a reorientation of motives. The resolution of well-defined biomedical problems is the principal objective. Adequate technological capability already exists for the resolution of many biological problems of this class.

The intent of the National Institutes of Health, in co-sponsoring this conference, is to urge that a systematic effort be made to direct our image processing technology toward significant biomedical targets. Unfortunately, while administrators are sometimes allowed to talk glibly about technological opportunities, we often prejudge issues as a result of a fundamental misunderstanding of the technologies involved. I have been troubled by a logical flaw in the suggestion that the subject matter of this conference be biological image processing.

This title was chosen because it seemed more appropriate than biological pattern recognition, which in my mind implies the analysis of data, possibly to the exclusion of data reduction. Recently, I have wondered whether or not the notion of image processing is itself restrictive; it may connote the reduction and analysis of natural observations but exclude from consideration two- or three-dimensional data which are abstractions of phenomena rather than the phenomena themselves. By way of example, I refer to the three-dimensional representations of protein molecules by Levinthal (1), and to our own work on the two-dimensional representation of planar chem ical structures. I feel impelled to make these observations because the program of the next two days has been defined in such a way as to exclude extensive discussions of formal pattern recognition research and does not speak directly to that domain of images which are nonnatural.

I should like to reflect upon the nature of the contribution of image processing technology to biomedicine. The question is, do we limit our expectations to tasks in which the technology has the capacity for replicating relatively low-order motor and perceptual capabilities—albeit at great speed and consistency—or are there additional opportunities? It has been suggested that the ability to separate signal from noise automatically has implications for improving the effective resolution of analytic and diagnostic instruments; this has not been undisputably demonstrated. Furthermore, it has been suggested that the ability to quantize data automatically from massive populations provides opportunities for empirical analyses which are unattainable from the standpoint of a non-automatic technology. One may also conclude that the availability of suitably quantized data from large populations will permit the development of stochastically based biological theories.

REFERENCE

1. LEVINTHAL, C., Molecular model-building by computer. Sci. Amer., 1966, 214: 42-52.

CONTENTS 1

CONTENTS 1

IMAGERY IN ANALYTICAL METHODS

SURVEY OF IMAGE PROCESSING PHASES

AUTOMATIC SCREENING OF METAPHASE SPREADS FOR CHROMOSOME ANALYSIS*

THE APPLICATION OF CHARACTER RECOGNITION TECHNIQUES TO THE DEVELOPMENT OF READING MACHINES FOR THE BLIND*

AN AUTOMATED SYSTEM FOR GROWTH AND ANALYSIS OF BACTERIAL COLONIES’

AUTOMATIC PROCESSING OF MAMMOGRAMS§§

AUTOMATIC DIFFERENTIATION OF WHITE BLOOD CELLS†††

APPROACHES TO THE AUTOMATION OF CHROMOSOME ANALYSIS •

ADVANCES IN THE DEVELOPMENT OF IMAGE PROCESSING HARDWARE’

DIGITAL VIDEO DATA HANDLING: MARS, THE MOON AND MEN

BIOLOGICAL IMAGE PROCESSING IN THE NERVOUS SYSTEM NEEDS AND PREDICTIONS

SUMMATION AND PERSPECTIVE

For Biomedicine

For Hardware

INDEXES

NAME INDEX

SUBJECT INDEX

IMAGERY IN ANALYTICAL METHODS

MARY A. B. BRAZIER

WILFRID J. DIXON

University of California

Los Angeles, California

Biological research involves the study of living organisms. The necessary complexities of this research arise from the many variables which must be studied simultaneously, as well as from the transient nature of many observable conditions and the varieties of accommodation to the same stimulus.

Quantification in biology has proceeded slowly with the paucity of analytical methods and the lack or inadequacies of the mathematical models used to assist the observational and analytical processes. In the past, the biologist attempted to compensate for this lack by devising a variety of pictorial aids to his understanding; structures and forms too complex to describe were recorded pictorially. These pictorial aids include: (a) photographs taken in ordinary light in black-and-white or in color, either stills or in motion; (b) photographs recording diffracted or filtered light, opacity produced by various substances, transmission of radiation, passage of chemicals; (c) direct or photographic viewing of specimens prepared by smear, cross-sectional cuts; (d) derived pictures or images resulting from dichotomized or sealed threshold intensities from other pictures; and (e) various transformations of pictures or signals.

The quantification of these pictures is an emerging science in itself. Scanning devices, systems for computer guidance and analysis in the on-line mode, pattern recognition theories and mathematical and statistical techniques (these also assisted by computer) are now at a stage of development where their potential fusion will greatly accelerate basic research in biology.

This conference’s host institution has especial interest in this subject, for its Health Sciences Computing Facility1 has installed a Graphics Subsystem (the 2250 Display and the 2282 Recorder Scanner) which is supported for computation by a partition of the core of the IBM 360/75 computer. Portions of this equipment are not available commercially, and systems support as well as various applied programs have been developed by the facility.

PROJECTS IN PROGRESS

Among the many varieties of image processing applicable to biological research that are currently being explored at UCLA are a project in chromo some analysis being pursued jointly with the Radiology Department, a Fast Fourier Transform algorithm for analysis of electroencephalograms for the Brain Research Institute, and a graphics display of electric fields of EEG potentials for Dr. Brazier’s neurophysiological laboratory. In other words, activity is being developed not only in the transform of image to numerical data but also from numerical data to image display.

Chromosome Studies

Dr. H. Frey of the Radiology Department is testing algorithms for recognizing and classifying chromosomes and for identifying abnormal chromosomes resulting from genetic anomalies or radiation. The algorithms must provide techniques for identifying and separating chromosomes which touch or overlap one another, as well as means for categorizing the chromosomes from the computed descriptions derived from the presented pattern.

The program currently under development examines the field presented to it, searching for objects—for example, connected regions. Each object is classified as a single chromosome, overlapping chromosomes, or neither. Once an object is classified as a chromosome, the program defines a boundary for it, and classification can proceed. The goal of the project is the development of a package program for scanning chromosome photomicrographs, identifying and classifying the chromosomes found, and presenting the results. An example of the display is shown in Figure 1.

Figure 1. Chromosome display showing density ellipse.

The Fast Fourier Transform

A program for EEG analysis, based on the Fast Fourier Transform algorithm, greatly reduces the computation time previously necessary for spectrum analysis. The program, developed by Dr. R. Jennrich, estimates autospectra, cross-spectra, and coherences for stationary time series. Each series is decomposed into frequency components by means of a finite Fourier transform and the required estimates are obtained by summing products of the transformed series. Linear trend is removed from each series before transfor-

mation. If desired, series may be prefiltered (either by low-pass filtering or constructing an Ormsby filter) and decimated before detrending.

The user may control the flow of the program by selecting series to be analyzed, by constructing a desired filter, by choosing for simultaneous display the desired functions (amplitude of autospectra or amplitude, phase and coherence of cross-spectra) and by scaling the display. Output from the problems is stored in a temporary data set and may be recalled for comparison and simultaneous display. An example of an analysis of amplitude and coherence of two EEG records is shown in Figure 2.

Graphics Display of Electrical Fields on the Head

A research project of Dr. T. Estrin and Mr. R. Uzgalis in the neurophysiological laboratory of Dr. Brazier2 has resulted in programs for on-line spatiotemporal plots of the EEG. In conventional electroencephalography, the potential differences recorded between pairs of electrodes fixed to the head are displayed as amplitude functions of time; the spatial character of the electrical field must be inferred from the usual EEG tracings. Recent advances in computer-graphics permit the electroencephalogram to be viewed as a spatiotemporal presentation and should be useful in clinical and research electroencephalography.

In this new approach, a grid on a cathode ray tube is congruent with electrode positions on the scalp from which multiple channels of EEG data are amplified, multiplexed and digitized. The recorded voltages are spatially interpolated to complete the grid. An algorithm connects points of equal voltage and displays them as contours on the tube face of a 2250 graphics terminal which time-shares the IBM 360/75.

The contours in each display are considered as heights defining a topo

graphic surface with respect to a common reference. Successive displays recreate the time history of the electric field and are photographed by a motion picture camera under computer control. An example from a film is shown in Figure 3.

1 Supported by National Institutes of Health Grant FR-3.

1

2 The work of this laboratory is supported by grants from the National Science Foundation (# GP-6438), National Institutes of Health (# NB 04773) and Office of Naval Research (Contract # 233-69).

SURVEY OF IMAGE PROCESSING PHASES

GEORGE N. EAVES¹

National Institutes of Health

Bethesda, Maryland

DIANE M. RAMSEY²

Douglas Missile and Space Systems Division

Newport Beach, California

The title Image Processing in Biological Science was chosen for this conference with the expectation of providing a reasonable framework within which the organization of our discussions could develop. This choice was not necessarily an attempt to limit the scope of material under consideration. In all of our planning and deliberations, a distinction has been made between image processing and graphic analysis, both of which can employ pattern recognition techniques. While these two categories are not necessarily exclusive, the distinction between them serves to underline the main emphasis of this conference, while reflecting our recognition of important differences and interrelationships between the two fields or endeavor.

Perhaps the use of image as a descriptive rubric is partially misleading, for we are also concerned with the scanning of actual biological specimens, such as bacterial colonies. Within the context of our definition, we might include other specific examples of biological images, such as radiographs of tissues, electron micrographs, and optically enlarged histological cross sections and hematological material, or photographs of such slides. The workable parameters for interpreting our area of interest will include the use of a computer-controlled image scanner and computer-programmable analysis. For maximum developmental progress it is far more important that we stress the open-endedness of these concepts rather than force a set of premature definitions on an emerging field of endeavor; therefore, even the form of the image must not become a limiting consideration.

Prewitt & Mendelsohn (1) have specified five principal phases in the analysis of digitized images, defining them as: (a) delineation of figure and ground; (b) description of images by numeric and nonnumeric parameters, and by relational descriptors; (c) determination of the range of variation and the discriminatory power of these parameters and descriptors; (d) development of appropriate decision functions and taxonomies for classification; and (e) identification of unknown specimens. These five principal phases are closely analogous to the major phases involved in processing and classifying high-altitude or satellite pictures. Thus, the theoretical considerations in interpreting microimagery and high-altitude pictures are quite similar. Because of the urgency of national defense requirements, however, a higher level of effort has been devoted to research and development of techniques for automatic processing of reconnaissance photography than that expended on developing techniques for biological image processing. Much of the present-day technology for processing reconnaissance photography may be directly applicable to the processing of microimagery. A comparable level of effort has not been expended on developing techniques for biological image processing. Much of the present-day technology for processing reconnaissance photography may be directly applicable to the processing of microimagery, and these areas of similarity should be exploited fully.

With these considerations serving to guide the planning of this conference, we have utilized three broad categories to form a conceptual framework for viewing developmental accomplishments in biological image processing: (a) preprocessing to achieve image enhancement, (b) feature extraction to highlight properties considered important for correct recognition and subsequent classification of the image, and (c) design of an appropriate classification logic. The succeeding portions of this discussion will attempt to delineate the more general aspects of the various processing phases.

PREPROCESSING

The purposes of preprocessing or signal conditioning are (a) to emphasize or highlight aspects of the input signal which are deemed important, (b) to furnish in many cases a reduction in the amount of input data, (c) to supply a convenient input format for subsequent computer processing, and (d) to provide invariance. To accomplish invariance it is desirable that the classification assigned to an object or region in the field of view be reasonably independent of the position of that object in the field of view, the aspect at which it is viewed, the background against which it is seen, partial blocking of the object, and changes in illumination. Preprocessing techniques may include scanning, edge enhancement, enhancement of figureground contrast, Fourier transformation, and autocorrelation.

Consistent image quality is of salient importance to the accomplishment of maximum preprocessing. In the case of photographs, the image enhancement techniques to be discussed by Dr. Nathan and Mr. Selzer are especially appropriate. We find it particularly noteworthy that these techniques, although developed to meet the demands of a particular reconnaissance technology, were subsequently directed toward biomedical research through the cooperative interaction of alert scientists pursuing seemingly divergent technological objectives.

In the case of scanning directly from microscope slides, the problem of enhancement could be reduced by devising techniques that would assure maximum display of morphological features through preparative technology. For example, the preparation of white blood cells and the related histological techniques for demonstrating chromosomes may require a réévaluation of classic histological techniques. The utilization of biological competence is here an obvious requirement. The investigations to be discussed by Dr. Wald and Mr. Preston will include not only attempts to optimize the preparation of histological material but also the development of a program which can select automatically processable material from the microscope slide.

FEATURE EXTRACTION

An almost universal phase in pattern recognition of high-altitude pictures is the extraction of features or properties from the original signal. The process of extracting a property profile consists of making a number of decisions as to whether or not the property features are present in the input signal. Techniques for defining properties that carry significant information may be divided into those of either human or automatic design. In the former, the designer constructs property detectors for those features that are known or suspected to be important; this property list may prove to be inadequate, or it may furnish a format not suitable for the decision mechanism which often provides only for linear discrimination. Statistical property extraction, in which a sample of preclassified images is analyzed automatically, may be used to augment the known property list and to reformat the property profile into a form suitable for the decision mechanism chosen.

Within the context of feature extraction, Dr. Eden will discuss the application to cytological material of contour scanning techniques developed for reading printed text. Dr. Glaser will describe the use of contour scans for counting and analyzing large numbers of colonies of bacteria and other microorganisms and for identifying the organisms through observation of colony morphology and other characteristics observable during growth on solid media.

Dr. Ingram will discuss the use of the CELLSCAN system for recognizing cells by topographical analysis of the black and white computer-stored image of the cell. In contrast to this approach, Dr. Mendelsohn’s laboratory has 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. Dr. Macy will discuss the use of descriptive vectors that characterize local pattern and density areas in the automated diagnosis of breast tumors; a matching set of vectors related to the inherent symmetry of the two breasts permits detection of a tumor as an anomaly within its context by extracting patterns of disease from discarded, redundant patterns representative of normal tissue.

DESIGN OF CLASSIFICATION LOGIC

Nearly all current pattern recognition decision mechanisms essentially involve correlating the profile derived from the input pattern or image against one or more prototype patterns. Correlation schemes differ in the number of prototype patterns utilized, and in the means for specifying these paradigms. In most cases, the decision mechanism implements some form of linear discriminant function. In some instances, a quadratic decision surface has been employed to achieve separation of the pattern classes. There is increasing evidence that nonlinear discriminant functions may provide more appropriate decision rules for classification of biological images. In fact, it may be necessary to abandon traditional discriminant analysis procedures in favor of developing logical models for pattern analysis.

These alternative approaches to the design of appropriate classification logic are currently being considered and formulated at the Massachusetts Institute of Technology. Dr. Papert has been part of the group engaged in this work, and will discuss the problems inherent in designing appropriate decision mechanisms for pattern recognition and classification as well as likely solutions to these problems in the area of biological image processing.

It is expected that this conference will help not only to