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Workshop on Standard/Proceedings of a wo

QC100 .U57 NO.500-8, 1977 C.I NBS-PUB-C ICE & TECHNOLOGY:

WORKSHOP ON

STANDARDS FOR IMAGE

PATTERN RECOGNITION

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.500-8

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<-!-' r 77 \

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\ NBS Special Publication 500-8

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards
NATIONAL BUREAU OF STANDARDS

The National Bureau of Standards' was established by an act of Congress March 3, 1901. The Bureau's overall goal is to strengthen and advance the Nation's science and technology and facilitate their effective application for public benefit. To this end, the Bureau conducts research and provides: (1) a basis for the Nation's physical measurement system, (2) scientific and

technological services for industry and government, (3) a technical basis for equity in trade, and (4) technical services to pro- mote public safety. The Bureau consists of the Institute for Basic Standards, the Institute for Materials Research, the Institute for Applied Technology, the Institute for Computer Sciences and Technology, the Office for Information Programs, and the

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THE INSTITUTE FOR COMPUTER SCIENCES AND TECHNOLOGY conducts research and provides technical services designed to aid Government agencies in improving cost eff^ectiveness in the conduct of their programs through the selection, acquisition, and eff'ective utilization of automatic data processing equipment; and serves as the principal focus wthin the executive branch for the development of Federal standards for automatic data processing equipment, techniques, and computer

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" Located at Boulder, Colorado 80302.
Mijtjoric/i COMPUTER SCIENCE & TECHNOLOGY:

Workshop on Standards

for Image Pattern Recognition ^^^r]

Proceedings of a Workshop

^ iH^AJijOuyvv. ,xxo . C

Held at the National Bureau of Standards

Gaithersburg, MD, June 3-4, 1976

^ ^ 'J—

John M. Evans, Jr., Editor

Institute for Computer Sciences and Technology ,

> National Bureau of Standards

Washington, D.C. 20234

Russell Kirsch, Editor

Institute for Basic Standards

National Bureau of Standards

Washington, D.C. 20234

and

Roger N. Nagel, Editor

National Institutes of Health

9000 Rockville Pike

Bethesda, MD 20014

Sponsored by:

National Bureau of Standards

Electronic Industries Association

Institute of Electrical and Electronic Engineers

in cooperation with:

Association for Computing Machinery

•

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

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Acting Director

Issued May 1977
Reports on Computer Science and Technology

The National Bureau of Standards has a special responsibility within the Federal

Government for computer science and technology activities. The programs of the NBS Institute for Computer Sciences and Technology are designed to provide ADP

standards, guidelines, and technical advisory services to improve the effectiveness of

computer utilization in the Federal sector, and to perform appropriate research and

development efforts as foundation for such activities and programs. This publication

series will report these NBS efforts to the Federal computer community as well as to interested specialists in the academic and private sectors. Those wishing to receive

notices of publications in this series should complete and return the form at the end

of this publication.

National Bureau of Standards Special Publication 500-8

Nat. Bur. Stand. (U.S.), Spec. Publ. 500-8, 115 pages (May 1977) CODEN; XNBSAV

Library of Congress Cataloging in Publication Data Workshop on Standards for Image Pattern Recognition, National Bureau of Standards, 1976. Computer science & technology.

(NBS special publication ; 500-8) Supt. of Docs, no.: C13. 10:500-8.

I. Optical pattern recognition—Standards—United States. I. Evans, John Martin, 1942- II. Kirsch, Russell. III. Nagel, Roger N. IV. United

States. National Bureau of Standards. V. Title. VI. Series: United

States. National Bureau of Standards. Special publication ; 500-8. QC100.U57 no. 500-8 [TA1650] 602Ms [621.381 .9'598] 77-4773

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WASHINGTON: 1977

For sale by the Superintendent of Documents, U.S. Government Printing Office Washington, D.C- 20402 - Price $2.60 Stock No. 003-003-01771-0
TABLE OF CONTENTS

ABSTRACT v

INTRODUCTION vi

INTRODUCTORY ADDRESS 1

Dr. Ruth M. Davis,

Di rector

Institute for Computer Sciences and Technology

National Bureau of Standards

STANDARD TAPE FORMATS

Discussion _ 7

The NATO RSG-4/SGIP TAPE FORMAT 11 John S. Dehne

U.S. Army Night Vision Laboratory

Tape Formats 26

Theo. Pavlidis

Princeton University

Earth Observation Image Data Format 28

John Y. Sos

NASA Goddard Space Flight Center

IMAGE CONTENT AND STRUCTURE

Discussion 37

On Communicating About Pictures 41 Martin A. Fischler

Lockheed Palo Alto Research Laboratory

Syntactic Approach to the Description of Image Structure and Content . . 43

K. S. Fu Purdue University

Languages for Image Content and Structure 44

J. F. O'Callaghan

CSIRO Division of Computing Research, Australia

Constructive Descriptions of Images 45

Robert F. Sproull

Xerox Palo Alto Research Center

DOCUMENTATION OF THE RECORDING ENVIRONMENT

Discussion 51

Instrument Parameters in Image Digitization 52

Wayne R. Huelskoetter

DICOMED Corporation

Calibration of Television Microscopes 56

K. Preston, Jr. Carnegie-Mellon University

1 i i
Digitization of Ultrasonic Signals for Biomedical Investigation .... 59

J. F. Greenleaf, Ph.D.

Mayo Foundation

A Suggestion for the Calibration of Digitized Imagery 64

Werner Frei

University of Southern California

Factors Which Influence Acoustic Images of Medical Objects ....... 67

J. K. Zieniuk

Polish Academy of Sciences

Visiting Scientist at Bureau of Radiological Health

Documentation of the Recording Environment 71

M. Ritter

NASA Goddard Space Flight Center

M. S. Maxwell

National Aeronautics and Space Administration

Correcting Gray Scale Distortions in Photographic Images . . 77 Brent S. Baxter

University of Utah

The Recording Environment in Pathology 81 Lt. Boccia

Armed Forces Institute for Pathology

PROTOTYPE IMAGES

Discussion 85

Prototype Images - Selection Problems 89

Roger Nagel

National Institutes of Health

Prototype Radiographs 92

Jack Sklansky

University of California

Thoughts on Standardization of Parameters for Image Evaluation 99

Fred C. Billingsley

NASA Headquarters

PROGRAM 108

LIST OF ATTENDEES Ill

i v
ABSTRACT

Automatic image pattern recognition techniques have been successfully applied to improving productivity and quality in both manufacturing and service applications.

Automatic Image Pattern Recognition Algorithms are often developed and tested using

unique data bases for each specific application. Quantitative comparison of different

approaches and extrapolation of existing techniques to new applications is difficult

or impossible.

To facilitate data interchange in this area a two day workshop was held at the National

Bureau of Standards in Gaithersburg, Maryland on June 3 and 4, 1976.

The workshop considered the issues involved with interchange of images as data in standard formats on magnetic tape. Specifically, the workshop addressed the following

objectives:

1. To define mechanisms for achieving a standard format for magnetic tape interchange.

2. To define requirements for documentation of the recording environment of an image.

3. To recommend mechanisms for selecting and distributing prototype images.

4. To consider the requirements and to explore the prospect for a language to describe image content and structure.

KEY WORDS: Automation; calibration; data formats; documentation; image content language;

image processing; pattern recognition; prototype images; standards.

V
INTRODUCTION

Automatic image pattern recognition techniques have been successfully applied to improving productivity and quality in both manufacturing and service applications.

Automatic Image Pattern Recognition Algorithms are often developed and tested using

unique data bases for each specific application. Quantitative comparison of different

approaches and extrapolation of existing techniques to new applications is difficult

or impossible.

At the suggestion of the Electronic Industries Association (EIA) and with the support

and cooperation of the IEEE Computer Society Machine Pattern Recognition Group and the

Association for Computing Machinery (ACM) Special Interest Group on Graphics the National

Bureau of Standards conducted a two day workshop on problems concerned with the adoption

of the standards for the interchange of image pattern recognition data. The interests

of the various collaborating groups derived from concerns in the industry for advancing

the state of development in the pattern recognition and image processing art, concerns

in the engineering community for the adoption of standards for testing equipment and concerns in the computing community for the adoption of standards relating to the testing

of algorithms.

A steering committee consisting of the chairman of the various sessions of the workshop

developed, over a period of several months, a program which would deal with several

issues that seem central in various ways to the question of adoption of standards for

image interchange. The members of this group were:

William Alford, National Aeronautics and Space Administration

John Dehne, US Army Night Vision Lab, representing NATO

John M. Evans, Jr., National Bureau of Standards

Russell Kirsch, National Bureau of Standards

James B. McFerran, Sperry Univac, representing IEEE Roger N. Nagel , National Institutes of Health, representing EIA

Theo. Pavlidis, Princeton University, representing IEEE Judith M. S. Prewitt, National Institutes of Health, representing IEEE Azriel Rosenfeld, University of Maryland, representing the Journal Computer Graphics

and Image Processing

Gene Thorley, US Geological Survey ERDS Program

There were four distinct problem areas the workshop addressed itself to:

1. The adoption of standards for magnetic tape formats.

2. The documentation of the recording environment for image scanning and transducing.

3. The acceptance of prototype image data bases.

4. The description of image content and structure.

The first and most clearly necessary problem for concern in the workshop was the question

of agreeing upon formats for magnetic tape recording. Clearly any form of interchange

among research and development workers would be advanced by the existence of an agreed

upon format or set of formats for recording images when they are interchanged among

groups and between those producing image data and those using data for pattern recognition

and image processing purposes. This was therefore the topic of the first session of

the workshop.

The second session arose from a concern particularly in the academic research community

for documentation of the recording conditions under which images are produced. In many

cases images produced with specialized equipment, (particularly transducers and devices

which are peculiar to a particular environment) are exchanged between the laboratory

producing the data and other laboratories having no familiarities with the recording

equipment. Much valuable information is lost in the case where the characteristics

of the recording equipment and the whole environment are not documented. It was in an attempt to heighten sensitivity to the need for documenting the recording environment

conditions that this second session of the workshop was conducted.

vi
The third session addressed itself to the production of prototype data bases. There

are at least two clear needs for such prototypes of data bases. The first is the analog

of the problem in testing ordinary optical instrumentation where test patterns are used

for specifying such properties as resolution. For testing various types of devices the

need for prototype data bases is fairly evident. So are prototype data bases necessary

for testing algorithms since the useful intercomparison of algorithms for doing pattern

recognition could be enhanced if agreed upon prototype data bases could be interchanged

for testing these algorithms.

The fourth session of the workshop concerned with describing image content and structure

arose from a need expressed occasionally in the academic community where images of a very specialized sort are recorded. Typically these images are produced in laboratories

where specialized talents exist for interpreting those images. Obvious examples occur

in the biomedical community. When these images are interchanged between the producers,

typically biomedical scientists, and the computer scientists attempting to do pattern

recognition research, there is often a failure to communicate, along with the images,

the suitable descriptions of the articulated structure sufficient to enable the computer

scientists to understand what the content and structural descriptions of these images

are. It was in an attempt to investigate the possibility of compiling such structural

descriptions that this last section of the workshop was conducted.

The reader of these workshop proceedings will easily note that none of these four

areas are yet advanced to the stage where consensus exists, despite the widespread

recognition of the need for such a consensus. He will also note equally clearly that

the different areas of concern are in different stages of development. Perhaps this

differential status of the different areas can serve as a source of motivation for increasing

activities in those areas which are more backward and for encouraging the continued

pace in those areas which have already showed noteworthy progress.

John M. Evans, Jr. Acting Manager

Office of Developmental Automation

and Control Technology

Institute for Computer Sciences

and Technology

National Bureau of Standards

Roger N. Nagel

Senior Staff Fellow

National Institute for Dental Research

National Institutes of Health

Russell Kirsch

Head, Artificial Intelligence Research

Applied Mathematics Division

Institute for Basic Standards

National Bureau of Standards

vii

Introductory Comments

at the

Workshop on Standards for Image Pattern Recognition

June 3, 1976

by

Dr. Ruth M. Davis

Director, Institute for

Computer Sciences and Technology

I am delighted to welcome you to the National Bureau of Standards, and particularly

to this landmark Workshop on Standards for Image Pattern Recognition.

Your meeting here - to explore some of the issues in standards development for image

pattern recognition - is a most appropriate activity for the National Bureau of Standards.

NBS has long been involved in standards development and improvement, the interchange

of information and the application of technology for public benefit.

This year the Bureau is celebrating its 75th anniversary as the Nation's physical

science and measurement laboratory. Throughout its history NBS has provided the basis for

the Nation's measurement standards. In addition to a traditional role in the area of

weights and measures, NBS has long been involved in product testing and research on test

methods and specifications.

NBS has been at the forefront of new technologies in order to provide the foundation

for science and commerce and to effectively serve government and the public. Computer

technology is one of those areas in which NBS has been at the forefront. In the 1940'

s

and 1950' s Bureau scientists designed and developed SEAC, the first general purpose, stored

program computer operated in the U.S.

Continuing technological developments in ensuing years coupled with growing Federal

Government dependence on computers for information processing led to legislation in 1965

to establish Department of Commerce responsibility for improving the utilization of

computers by Federal agencies.

By that legislation, the Brooks Act, NBS is directed to set standards for Federal

procurement and use of computers, to advise other agencies on the efficient use of computers

and to carry out research and development in computer science and technology.

1
One area in which we carry out these functions is the application of computers in

automation technology. The particular aspect you will be discussing in this Workshop,

automatic image pattern recognition, is especially relevant to growing concerns about

productivity and quality of goods and services in the U.S. today.

There have been dramatic shifts in the make-up of the labor force in the U.S. over

the past few decades. Between 1960 and 1970 the increase of workers in the service

industries was about 12 times the increase of workers in the goods producing industries.

Today about 2/3 of the labor force is employed in the Service Sector which includes

Federal, state and local government employees and people engaged in services such as health

care, wholesale and retail trade, services and financial and legal work.

The rise in importance of the Service Sector has been accompanied by some disturbing

problems:

Productivity in the Service Sector has been lagging. The annual growth rate of labor

productivity in the Service Sector has been significantly lower than the growth rate in

the goods sector: 2.7% vs. 3.4% between 1960 and 1970.

Secretary of Commerce Elliott Richardson is especially interested in this aspect of

services. He says that available technology to improve productivity has not been adequately

applied, and he cites health care specifically as a service which he believes can be improved

by the application of technology.

Services have contributed to increased costs. As an example, the costs of health

care have increased faster than any other component of the consumer price index during the

past two decades. Between 1950 and 1974 medical costs increased by 180% while the CPI

rose by 105%, causing health care costs to increase from 4.6% to 7.6% of the GNP.

Salary increases in the Service Sector have outpaced increases in the goods sector.

In the years from 1953-74, pay increases in manufacturing were 141%, while increases for

State and local government employees were 188% and for other services 171%.

There is widespread dissatisfaction with the quality of services. A report on

Automation Opportunities in the Service Sector for the Federal Council for Science and

Technology found that about 87% of the consumer complaints received by Better Business

Bureaus and the Office of Consumer Affairs were directed toward the service industries

with 66% directed toward services actually bought in the marketplace.
Automation technology can play a key role in attacking the problems of the Service

Sector by improving productivity and quality of services.

The application of automation in agriculture has greatly improved productivity. As a

result we spend the smallest percentage of our annual income for food of any nation in the

world, and, in addition, we provide for most of the food aid shipments in the world.

Automation in manufacturing is widespread with applications of assembly-line

automobiles and appliances. Numerically controlled tools, industrial robots and computeraided manufacturing techniques are now beginning to be used in the rest of manufacturing

industry, with potential increases in productivity running to thousands of percents.

The principal experiences with automation in the Service Sector have been in the

applications of computers and the mechanization of paper handling. The capability of

computers to process information quickly and accurately is utilized by service industries

such as banking, credit, financial and insurance. Most large organizations use computers

for payroll calculations, health and employee records and financial and inventory records.

Real-time services such as air travel are dependent upon computers. There have also been

successful applications of automation processes for automated bank tellers, garbage

collection, automobile diagnosis, automated warehouses, vending machines, direct distance

dialing and computer-assisted instruction.

j Automatic image pattern recognition is one aspect of automation with particular

I

j significance for improving productivity and improving quality in the Service Sector.

j Image pattern recognition technology has been successfully used to automate fingerI print identification, weather prediction, photographic interpretation , and molecular and

cellular pattern analysis.

Automation, via image pattern recognition, has the potential for improving

productivity and services in many additional areas such as:

° automation of analyzing x-rays and cytology: applications in the health field

° space applications and resource discovery and management

: ° safety systems for public transportation systems

ij ° automated mail and parcel post recognition and handling systems in the post office

II ° automated maintenance and repair systems for consumer services

° automated security systems

; ° inspection and quality control for manufacturing industries

' 3
However, the barriers to the successful diffusion of automation to these and other

service and manufacturing applications are many. Research and development in automation

technologies are fragmented and the lack of standards hampers the transferability of

existing technology.

Very often, applications are developed using unique data bases, making transfer of

existing techniques to new applications difficult or impossible.

The development and diffusion of image pattern recognition technology requires a

coherent technical foundation or infrastructure that allows people to communicate with

others and to efficiently utilize available technology. That is why this Workshop is so

important. We are hopeful that additional attention to standards for image pattern

recognition will advance research and development in this field by improving communication

and data interchange. This, in turn, will help government agencies and private sector

organizations to improve productivity and service quality by applying this technology more

rapidly and more efficiently.

I wish you success in your Workshop. I hope that you enjoy this visit to the

National Bureau of Standards and that you will come back again.

4
STANDARD TAPE FORMATS

Chairpersons: John Dehne, Army Night Vision Lab

William Alford, Goddard Space Flight Center

Panelists: Theo. Pavlidis, Princeton University

John Sos, National Aeronautics and Space Administration

Sandra Hawley, ESL Incorporated

5

Discussion

The general consensus of the attendees suoported defacto adoption of a limited number

(1-5) tape format standards. This was evident both in the general discussion following

the presentations and in the resiolts of a survey questionnaire . There was, however, considerably less unanimity as to the detailed design of such formats.

One major area of disagreement centered around the issue of whether it is practical

to have one standard tape format or not. Those favoring the adootion of a single standard

(one third of questionnaire returned) generally favored choosing a self-documenting

meta-standard with sufficient flexibility for a broad variety of applications. A majority

of this grouD (according to the questionnaires ) ranked the NATO standard most highly and

felt that such a standard oould be adopted within their ovm organization in one year or

less. Those favoring more than one standard generallv anticipated greater time delays

(2-3 years) before a standard could be adopted within their own organization.

A key issue within this group is the desire for a format which can be read and written

by ANSI FORTRAN programs. "This is important to users vtio lack svstems prcxjramtdng

experience or support and tends to place several restrictions on format design as follows:

1) There is great resistance to incorporation of the self documenting

header records needed for a meta-format in the same file as the

image. These users orefer the use of a header file (which

generally reduces the data integrity of the meta-format anproach)

so that available system functions may be used to skip it - as

opposed to the user program functions needed to by-pass similar

header records.

2) Single file (image sans header) tapes are preferred by some in

this group due to system/programmer difficulties with multi-file

tapes.

3) Fixed record length is required to insure that tape blocking

can be oontrolled at a JCL level on all operating systems. In

addition, the length should be small (2K-4K bytes) to allov?

buffering on minicomputer systons and should probablv be an

integral multiple of some small number (128-256) to allow use

7
of long records via format buffered I/O using system logical/

physical record blocking control. Use of a fixed record

length on all files in the same tape is desirable, but not

necessary.

4) This group shows strong preference for simnle formats vdiich

could be used for processing within an installation as well

as for transmittal between installations. Thus use of any

line by line ancillary data (line numbers, non-video calibration levels, etc) is disliked. This group generally

favors the use of formatted (alpha-nijmeric ooded) or binary

vjord length data over other schanes for coding the picture element

values - though differences in machine codes and vrord lengths

makes these approaches difficult as well. The hooe here is

to use I, E, F, or A formats.

With all these factors it was generally concluded that it might be possible to

generate one or at most two candidate formats v*iich could then be published for critical

review and v±iich would form the basis for any further standardization effort. To this end

interested parties representing the entire spectrum of opinions reconvened about one week

later to attempt to formulate the candidate formats. The simple format was designed first

and may be described as follows:

The Simple Format

1. No headers or other ancilliairy information on the tape. All such data is transmitted

as separate written documentation which is sent with the tape.

2. Each file contains one picture. Each picture is a regular two dimensional array of

single valued inteaers (i.e. only 1 spectral band per picture)

3. Each image line is one record and all records in a given file are the same length.

The maximum record length is 4096 bytes. (1 byte = 8 bits)

4. Each picture element (pixel) is encoded as a positive integer in one byte. Picture

elements within a line are stored sequentially with a record.

5. Multi-file (multi-picture) tapes are permitted. Record lengths may vary between two

files on the same tape. Files (pictures) are separated by one EOF mark. The last file

8
is followed by two or irore EOF marks.

There was much discussion on the topic of a meta-format and an attempt to design one.

A major conceptual difficulty raised was the desirability of having yet another proposed

meta-format, especially one proposal bv a group not actively involved in generating or

exchanging large data bases. It was generally concluded that such an attempt might not be

wDrth#iile, especially in light of continuing internationally coordinated activities by

NATO and NASA, unless it resulted in a format near eno\agh to one or more of the existing

ones to cause an acceptable design impact. Candidate meta-formats include the MATO format,

the NASA VICAR format, and the NASA nroposed nev? CCT format (see SOS naper) . Each has

its strong and weak points.

The NATO format has been internationally coordinated and is the only one that makes

provision for transmittal of non-imagery data, real and conplex valued imagery, and negative

value image points. It is a very good exanple of a meta-format, especially in light of

the fact that image format and tape format are ccnpletely decoupled and treated as separate

issues. This allows great flexibility including very long image lines with simultaneous

limits on maximum tape buffer length. However, this format places limits on ancilliary

data (none in an image line and onlv 4K max. characters in free format per image header)

v^ich severely limit its utility in NASA type data bases.

The NASA VICAR format is currently the only one in operational use and allows

unlimited amoijnts of ancilliary data per image. However, it has only been used to date

as a processing and storage (not transmittal) mechanism and is currently quite machine

dependent in pixel storage codes. Further it is not designed for use with non-image data

(feature vectors, etc) and retains only limited flexibility for distinctions between tape

and image formats.

The proposed new NASA CCT format can be expected to be very heavily used and widely

dispersed owing to large scale distribution of the tapes by NASA. It is currently the

least settled (hence easiest to impact) of the three. As currently proposed it would

allow maximal use of ancilliary data, but would only contain provision for transmittal of

positive integer, imagery type data. A relatively inflexible relationship between tape

and image format is proposed vd-iich would make use of smaller tape buffers more difficult.

Furthermore, most applications do not require such extensive ancilliary data (line by line)
and many users are quite concerned about finding simple ways to eliminate it frcm the tapes

they use.

After lengthy discussion centering around a varient of the VICAR format it was

generally concluded that design of a separate new meta-format was not reasonable in light

of the \ancertainties of acceptance bv the current major users of such formats. As a

result, no meta-format was drafted. However, it is hoped that the oonments about existing

meta-formats may help to guide their further developments.

10
The NATO RSG-4/SGIP TAPE FORMAT

By

John S . Dehne

US Army Night Vision Laboratory

Acting US Project Officer, NATO RSG-4/SGIP

Under the NATO Defense Research Group (DRG) cognizance of and cooperation in the

field of Pattern Recognition is covered by AC/243 (Panel III) Research Study Group 4.

In recent years, this group (RSG-4) has been very active in assessing various military

application areas for pattern recognition and in fostering cooperation and coordination

among participating governments in each of these areas. Dr. David Hodge of the US Army

Human Engineering Laboratory serves as the US Delegate to RSG-4.

The first area which RSG-4 assessed was Image Processing. A substantial interest

for cooperative efforts in this area was discovered among all participants. As a result

a Subgroup on Image Processing (SGIP) has been formed to plan and coordinate cooperative

projects. This group consists of a Project Officer from each participating country.

Mr. John S. Dehne of the US Army Night Vision Laboratory is currently the Acting US

Project Officer.

It was immediately clear to all participants that cooperative efforts would depend

on ready interchange of image data bases and algorithms. This interchange was hampered

by the fact that each installation had generated its own image processing software system

specifically for its own computer facilities which differed enormously. Thus, each

installation had developed one or more unique tape formats and based a large software

and data base investment on the use of that format. Because of this, adoption of a

single format for use in all installations would have been quite expensive. Instead,

it was decided to develop a tape format to be used only for transferring digital imagery

from one installation to another. Each installation need write only two simple programs

to begin exchanging imagery - one to translate between the transfer format and the format

and the format of that specific installation and another to do the reverse.

11
The choice of this approach, development of a transfer format, had two other very nice

aspects. First, it relaxes some of the constraints which must normally be considered in

designing a tape format for imagery. For instance, since the format is to be designed for

transferral of image data rather than storage, constraints on packing density are somewhat

relieved. Similarly, since the format does not have to be used for the actual processing

of the images, constraints relating to the use of headers, trailers, and attempts to design

for minimal tape motion and processing time are also relaxed.

Second, development of a format just for transferral of image data tends to focus

attention on the real problem - transfer of all image related data. In this light it

becomes important to consider the transferral of other imagery related data when designing

the format. Such data include image transforms, calibrated imagery (which may be composed

of non- integer values), feature vectors, classification parameters, documentation of the

recording environment, and even the image processing source programs.

It was with these considerations in mind that the RSG-4/SGIP developed a draft format

at its first meeting in July 1975. The NATO group was also made aware of similar efforts

then on going in the US by the EIA and an IEEE panel on Biomedical Pattern Recognition.

The draft format was circulated to all contributing defense laboratories in participating

countries, and was made available to the EIA and IEEE group in the US for comment. This

resulted in the final approval of the format as shown below at the second RSG-4/SGIP

meeting in February 1976. Experimental testing of translation programs is scheduled to

begin shortly with the exchange of a test tape able to flex all options ( actually two

tapes - one 7 track and one 9 track) . This tape will be circulated among participating

countries before the next meeting now scheduled for November 1976.

; Several things should be noted about the format. First, it is generally entirely

self-documenting. This is achieved by the use of two headers at the start of each file.

Header 1 documents the origin, structure and data organization of the file in a fixed

format. Header 2 allows free format documentation of all other parameters pertinent to

the data (e.g. documentation of recording environment, location of significant image

features, recording of image processing techniques already applied, etc.). This is

12
achieved without the need of extra EOF marks, which usually cause a problem in such cases

by making each header a record rather than a separate file. Thus, header processing may

be ignored, if the file structure is otherwise known, merely by skipping the first two

records of each file.

Second the format is unusual in that it separates the format of the data as recorded

on the tape from the format of the data when considered to be an imager. Thus, while each

image (which may be multi-spectral) is written on the tape as a separate file (there may

be one or more on a tape) , it is not necessarily true that each record contains one image

line (though this is a particular form) . This not only allows the format to be used for

transferral of data other than images, but also allows very long image lines (e.g. 12

channel, pixel interleaved, 4k by 4k pictures) to be transferred without requiring overly

large tape buffers (4k max. allowed) . This makes the format applicable to small computer

owners as well as those with large installations.

The basis for the entire tape format is the tape character or byte. This is taken

to be 8 bits on a 9 track tape and 6 bits on a 7 track tape. Alphanumeric data is encoded

in BCD on 7 track tapes and in a truncated version of ASCII on 9 track tapes (one tape

character or byte in either case) . This results in a format specification for both 7 and

9 track tapes which can be read on a very wide range of machines (French MITRA, IBM, CDC,

DEC to name a few) and which requires actual translation (not just repacking) to convert

between 7 and 9 track tapes.

Header 1 may be broken into three principle parts based on what is being described.

Bytes 1-24 concern the origin and identity of the image. This includes bytes 17-24 which

is an 8 byte alphanumeric identifier given uniquely to this particular image file by its

originator. Bytes 25-80 describe the format of the rest of the data in the file.

Bytes 81-104 detail the format of the data in the image (x and y sizes and type of data)

.

Bytes 105-128 are currently unassigned and remain for future modification and expansion

(some could be used to specify multi-image files, for instance, though this is not

currently under consideration)

.

13
Header 2 is in free format and constrained to non-zero length to encourage good

documentation. Exact contents of Header 2 will depend on the important factors of the

problem at hand.

The basis of the format for the actual image data is the integer format. Integers

are recorded in ones complement binary in consecutive tape bytes, most significant byte

first on tape. Real numbers are recorded as separate integers for mantissa and exponent

(both to base 2 with no bias) where the length (in tape bytes) may be different for

mantissa and exponent. The exponent is recorded first. Complex numbers (which may be

recorded as integers or reals) and multi-channel pixel interleaved data are straightforward expansions of the basic format.

The resultant format can be generated or read using CDC 6600 utility programs,

and US standard fortran subprograms are being written by the author to effect the

translations in an almost machine independent fashion. The only machine dependent

portions will be one routine to read or write the long tape records required (impossible

to do with A formatted or unformatted reads and writes on many machines) and one routine

to create the ones complement format for numbers from the internal format of the machine

being used. Both these routines may require assembly programming depending on the

installation. In addition, positioning to the correct file will remain installation

dependent and it is recognized that some installations may only be able to handle single

file tapes.

Distribution of tapes within the NATO, RSG-5/SGIP group will be made on a bilateral

decentralized basis as one participating installation requests data from another directly

or through the national Project Officers. The Project Officers will however make surveys

of the imagery data available for transferral from their countries. This information will

be available to all participating installations, in all countries. The effectiveness of

this distribution method remains to be tested and may depend on the specific projects

undertaken and the schemes used to manage them - which are not yet determined. In any

case, it may be the only viable scheme within the equalLtarian framework of NATO.

14
NATO (RSG-4) Tape Format

General : Data is recorded on 1/2 inch wide magnetic computer tape.

Format allows either 7 or 9 tracks at 800 BPI, NRZI

Maximum record length allowed is 4k. tape characters (6 or 8 bits)

A tape contains one or more files.

Files are separated by single EOF marks.

Last file on tape is followed by at least two EOF marks.

Each file contains a whole image. Images too large for one tape must be

divided into sub-images which are then handled as individual images.

Each file consists of two header records followed by N image data records.

Header 1 : The 1st record of any file (Header 1) is 128 bytes long.

Header 1 is coded in BCD for 7 track tapes, even parity and in ASC II

(modified) for 9 track tapes, odd parity.

Allowed characters are shown below:

Equivalent Octal Number

Symbol 7 tracks 9 tracks

0 12 060

1 01 061

2 02 062

3 03 063

4 04 064

5 05 065

6 06 066

7 07 067

8 10 070

9 11 071

A 61 101

B 62 102

C 63 103

D 64 104

E 65 105

15
Equivalent Octal Number

Synibol 7 tracks 9 tracks

F 66 106

G 67 107

H 70 110

I 71 111

J 41 112

K 42 113

L 43 114

M 44 115

N 45 116

0 46 117

P 47 120

Q 50 121

R 51 122

S 22 123

T 23 124

U 24 125

V 25 126

W 26 127

X 27 130

Y 30 131

Z 31 132

13 075

73 056

/ 21 057

( 34 050

) 74 051

t 53 044

* 54 052

33 054

' 14 047

16
Equivalent Octal Number

Symbol 7 tracks 9 tracks

40 055

+ 60 053

•b (blank) 20 040

new line 57 015

A symbol is recorded in one byte on the tape.

Header 1 contains the following information:

Bytes Meaning

1-8 Identity of Originator: 4 symbols for country, 4 symbols

for the laboratory.

Example: USA NVL

9-16 Date of Data: Year Month Day

Example: 76 06 03

17-24 Name/Number of File/Image

Example: GRAF PGl

25-32 Number of Records/File (R)

(R = N+2 to account for 2 header records)

33-40 Number of tape characters in header 2 (L2) (L2>0 required)

41-48 Number of tape characters/image data record (L3)

49-56 Number of channels /sample (C)

(One channel complex data is treated as two channel

data with C = 2 x no. of channels)

57-64 Number of tape characters/channel for integer data (I)

(1=0 for real values)

65-72 Number of tape characters for mantissa of a real valued

channel (E^)

(E^ = 0 for integer values)

73-80 Number of tape characters for exponent of real valued

channel (Eg)

(Eg = ) for integer values)

17
81-88 Number of samples per line (S)

89-96 Number of lines per image (LI)

97-104 Type of data values (T)

T = 0 non-complex, integer

T = 1 complex, integer

T = 2 non-complex, real

T = 3 complex, real

105-128 Currently not assigned

Header 1 example: An image of 1024 x 1024 sample points has 32 different grey

levels for each channel (1-6 bit tape character)

The image is registered in 3 colors. Header 2 is 128 bytes

long. Image generated today.

Bytes Contents Alternate tontents

1-8 USA NVL USA NVL

9-16 76 06 03 76 06 03

17-24 EXAKPLEl EXAMPLE2

25-32 t5^bb770 00001026

33-40 00000128

41-48 -l5W3b4096 00003072

49-56 i5bbbt)t)b3 00000003

57-64 -bbbbbbbl 00000001

65-72 t)bm-bbbO 00000000

73-80 00000000

81-88 bbbbl024 00001024

89-96 ^^1)1024 00001024

97-104 -bb-bb^^-bO 00000000

105-128 Blanks or zeros

18
Header 2 : The 2nd record of any file contains a free form description of the image.

This may include specification of the physical significance of the

various channels, description of scanner, meterological conditions

when image taken, location of objects of interest in image, etc.

Header 2 has variable length as specified in Header 1 (L2)

(0<L2-<4096 tape characters)

Coding is same as header 1

.

Image Data : Image data is coded in binary, odd parity, one's complement.

Integer format is basic to all others.

Integer Format : Most significant part is recorded as 1st tape character.

Most significant bit of 1st tape character is sign bit

(0 =^positive, 1=^ negative)

Most significant bit is the left most bit

Example: For 2 tape characters/channel, 7 track tape:

5 -^000000000101

-5 -> 111111111010

Real Format : The exponent and mantissa (base 2) are reduced to integers. Both

these are then stored as separate integers on the tape for

each sample.

First Eg tape characters for the exponent, then Ejj, tape characters

for the mantissa.

Example: lA.S^o = OIIIO.I2 x 2° = OIIIOI2 x 2"!

lEXP = 111110 IMANT = 011101 where Ep = E„ = 1 '-' m

and 7 track tape is used.

Complex Format : Integer values (T=l) stored as integers. Real values (T=3) stored

as real values. Each channel consists of two values. The

first value is the real part, the second is the imaginary part.

Example:

Real Imag Real

X

Imag Real Imag

Ch 1 Ch 2 Ch 3

19
Multi-Channel Data : Non-registered data stored as separate images in separate

files

Registered data stored on pixel interleaved basis.

(For one sample the values of the different channels are recorded adjacently).

Examples: 3 color, non-complex data

( Chi

[

Ch2 Ch3 Chi Ch2 Ch3 Chi

Y

/ Sample N Sample N+1

Chi

2 channel, complex data

Ch2 Chi

r \r - - ^

Real Imag Real Imag Real Imag Real Imag n V J . ,

^ Sample N Sample N+1

20
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22
23
Image Tape Formats Questionnaire

1. Please specify your organizational affiliation:

Academla Industrial Governmental Other

Specify

2. Please characterize your Image processing Interest (s):

^Biomedical ERTS, LANDSAT Real Time Sensors (FLIR,TV, etc.)

^OCR Reconnaissance Fingerprint, Handprint

Other

Specify

3. Do you think It Is possible to have one standard format for all applications?

Yes No

How desirable is it to have only one standard?

The only good way The worst possible way

5 4 3 2 i

4. Do you favor a limited number of standard formats for different application areas?

Yes No

If so, how many do you think would be required?

5. Would your organization be willing to use a defacto standard? ^Yes No

If so, how long would adoption of such a standard take?

1 mo. or less 1 yr or less 2-3 yrs 5 yrs or more

6. Is your organization an Image data consumer or producer?

^Consumer ^Producer

What sort of image data might your organization furnish?

What sort of image data would your organization desire to receiver?

24
What is the best method for distributing image data tapes?

One or a few central distribution points

Exchange between individual institutions

Both of the above

Other

Specify

8. An acceptable format must include the use of:

9 track tapes

7 track tapes

DECTAPES

Other

Specify

9. Comment on the strong and weak points of the formats presented and indicate your

preference:

a. NATO format (Dehne) Best Least Useful

b. Large Image Format (SOS) Best Least Useful

c. User Format (Hawley) Best Least Useful

d. Biomedical Format (Pavilidis) Best Least Useful

10. Additional Comments (Use back of sheet if necessary)

:

25
Tape Formats

by

Theo Pavlidis

Dept. of Electrical Engineering and Computer Sciences

Princeton University

Last year a task force on Data Bases and Portable Software was formed within the

Biomedical Pattern Recognition Subcommittee of the Machine Intelligence and Pattern

Analysis Committee of the IEEE Computer Society. The problem of tape format came up

quite early in our deliberations and after considerable discussion we felt that it was

best to avoid specifying the format in too restrictive a fashion and instead rely on

good documentation. The following general specifications were adopted.

Tape and Data Format

(1) The data should be stored on h inch magnetic tape, NRZI mode, odd parity,

IBM compatible, 7 or 9 tracks, with 9 tracks preferable.

(2) Suggested density 800 bpi with 556 bpi and 1600 bpi also acceptable.

(3) Block size (physical record length) should not exceed 4K (4096 8-bit bytes).

(This requirement was imposed in order to allow the reading of tapes by

minicomputers with limited buffer size).

(4) First file on the tape should specify in EBCDC or BCD format the contents

of the tape.

(5) Each item must be preceded by identification, preferably in separate

record.

(6) For gray scale pictures of size greater or equal to 64 x 64, each pixel

should be a byte, each line a physical record, each picture a file.

(7) EBCDC or BCD formats are preferable to binary.

26
The above specifications are quite loose and can hardly qualify as a standard.

However there might be situations where even they can still be too strict. For example

there are applications where one deals with very large binary pictures, 512 x 512 or

even greater. Printed wiring boards are one such instance. Storing one pixel per byte

would require 8 times as much space as storing the maximum 8 pixels per byte. If an

unpacking program is available, the information can be retrieved easily. Such a program

should be also part of the processing one since storing in core 512 x 512 bytes can be

quite a problem for most installations.

This points out the problems which must be dealt with in establishing standards.

The variety of picture sizes and types makes a universal standard impossible. At one

end we may have data bases consisting of many thousands of small (e.g. 24 x 24) binary

pictures. At the other end we may find very large (e.g. 1024 x 1024) grey scale pictures,

like radiographs with each data base consisting of not more than 100 of them. The one

picture per file specification may be reasonable in the second case but not in the

first. Also bit packing is possible only in the first case. (IEEE Data Base 1.2.2 which

contains alphanumeric data is of this nature and it packs 3 bits per integer. The bit

retrieval is quite easy using a high level language like FORTRAN).

A major problem in reading tapes from other installations is the handling of special

symbols entered by the operating system when a tape is written by a high level language.

Binary tapes would not pose this problem if written with proper care. However such tapes

would be very difficult to read when transferring data between machines having bit-byte

configurations (e.g. IBM and CDC).

It seems that we may need more than one standard and that for the immediate future

the best hope in facilitating transfer of data bases may be good documentation. The

establishment of separate standards for each application (e.g. satellite pictures,

radiographs, alphanumerics etc.) may also be a possibility. Areas where a standard

has not been established could use one from their "nearest neighbor."

27
Earth Observation Image Data Format

by

John Y. Sos

Head, Image Processing Branch

NASA Goddard Space Flight Center

Greenbelt, Maryland 20771

Experience with processing and disseminating Landsat imagery within the NASA Data Processing Facility (NDPF), located at the Goddard Space Flight Center (GSFC), has been

applied to the development of a flexible format for Computer Compatable Tape (CCT) con- taining multispectral Earth observation sensor data. The driving functions which com- prise the data format requirements are summarized in Table 1. Using these drivers as

a guide, coupled with four years of experience in Landsat image processing, the following

general data format guidelines emerge:

• Open workspaces must exist within the tape format so that all

subsystems in a data processing chain could easily append, delete or modify information

• The tape format should be organized and segmented along the

following logical lines:

1. The first information encountered should be introductory

and provide self identification standards in byte convention

2. Afterwards a generic description of all data fields and

data within those data fields should be provided in standard byte conventions

3. Specific information should be provided to decode and

interpret data fields and data within those data fields

in byte conventions

4. Ancillary and annotation information should be provided

in compact form to accompany the actual imagery

5. The imagery itself should be presented in a compact form

with a minimal amount of unique identification

6. The blocking of data should be optimized around the units

of imagery acquisition

7. A summary should follow to conclude a major image data set

The implementation of the above guidelines are shown in Figures 1 through 3, Figure

1 illustrates the organization and structure of individual CCT records. Figure 2 shows

a high level layout of a multispectral CCT with detailed CCT features shown in Figure 3. Examples of three possible arrangements (or interleaving) of Landsat MSS image data are presented in Figure 4. More detailed information concerning these features are found in the "Quick Look Processor CCT Data Format Specification" dated July 31, 1975, prepared

under contract NAS5-24033, Mod 26 for GSFC by Operations Research, Inc.

It is planned to introduce the proposed formats in 1978 as a new digital Image

Processing Facility (IPF) becomes operational at GSFC. As a first step in the systematic

approach to image data format standardization, a data format which meets the general re- quirements will be introduced by NASA, the U.S. Department of Interior's EROS Data Center,

28
TABLE 1 EARTH OBSERVATION IMAGE

DATA FORMAT DRIVERS

• MULTIPLE IMAGE SENSORS

- LANDSAT (MSS, RBV)* - NIMBUS (CZCS, THIR)* - SMS (VISSR)*

• VARYING SENSOR CHARACTERISTICS - NUMBER OF SPECIAL BANDS (1-10) - NUMBER OF PIXELS/LINE (1,000-20,000) - NUMBER OF LINES/SCENE (1,000-20,000)

• MULTIPLE USERS

-R&D

- PRODUCTION

• VARIOUS USER PROCESSING REQUIREMENTS

- MULTISPECTRAL CLASSIFICATION - BLACK & WHITE FILM PRODUCT GENERATION

- FALSE COLOR COMPOSITES - SPATIAL FILTERING - PATTERN RECOGNITION

• DATA VOLUME

- HIGH IMAGE DATA VOLUME (10^) - LOW ANCILLARY DATA OVERHEAD

• MULTISPECTRAL IMAGE DATA FORMATS

- BAND INTERLEAVED BY LINE (BIL) - BAND INTERLEAVED BY PIXEL (BIP) - BAND SEQUENTIAL (BSQ)

MSS - MULTISPECTRAL SCANNER

RBV - RETURN BEAM VIDICON

CZCS - COASTAL ZONE COLOR SCANNER

THIR - TEMPERATURE HUMIDITY INFARED RADIOMETER

VISSR - VISIBLE AND INFARED SPIN-SCAN RADIOMETER

29
FIGURE 1. CCT RECORD FORMATS

RECORD

NUMBER

D ppn D n n c L. U n U TYPE

CODE

SPARE

1 OPTIONAL

DATA FIELD 1 ZERO

1 FILLER

1

1

I 2 BYTES

- BYTE

1

1

1 BYTE

j

1 RECORD

NUMBER

RECORD

TYPE CODE

SPARE

ALPHANUMERIC BYTES OF TAPE DIRECTORY | OPTIONAL

DATA 1 ZERO

"'^"^

1 FILLER

RECORD

NUMBER

RECORD

TYPE CODE

SPARE

OPTIONAL

ALPHANUMERIC BYTES OF HEADER GROUPS A-E ZERO

FILLER

RECORD

NUMBER

RECORD

TYPE

CODE

SPARE ALPHANUMERIC BYTES OF HEADER GROUPS A-E

RECORD

NUMBER

RECORD

TYPE

CODE

SPARE

OPTIONAL

GROUP E DATA CONTINUED i ZERO

FILLER

RECORD

NUMBER

RECORD

TYPE CODESPARE

1 OPTIONAL

ANCILLARY DATA ZERO

FILLER

RECORD

NUMBER

RECORD

TYPE CODE

SPARE SCAN LINE

ID

CLAIBRATION/

QUALITY

DATA

OPTIONAL

IMAGE DATA ZERO

FILLER

RECORD

NUMBER

RECORD

TYPE

CODE

SPARE SCAN LINE

ID

j OPTIONAL

IMAGE DATA ZERO

FILLER

(48 BITS) ! (6 BYTES)

j RECORD

NUMBER

RECORD

TYPE CODE

SPARE

1 OPTIONAL

TRAILER DATA ZERO

FILLER

STANDARD

RECORD

FORMAT

TAPE DIRECTORY

HEADER

(CASE 1)

HEADER

(CASE 2)

ANCILLARY

IMAGE

IMAGE

TRAILER

30
FIGURE?.

HIGH LEVEL REPRESENTATION OF A MULTISPECTRAL CCT

BAND 1

BAND 2

IMAGE 1

BAWD N

IMAGE 2

HEADER (BAIMD 1) ANNOTATION

ANCILLARY

IMAGE

TRAILER

HEADER (BAND 2) ANNOTATION

ANCILLARY

ANCILLARY

IMAGE

TRAILER

HEADER (BAND 1) ANNOTATION

ANCILLARY

IMAGE

TRAILER

31
FIGURES.

DETAILED CCT FORMAT

HEADER RECORD 1

1 D P mil

\/vvvv\/> HEADER RECORD M

IRG TAPE DIRECTORY APJNOTATION RECORD 1 IRG EOF

•

AWIMOTATIOIM RECORD Q

IRG DATA SET 1 ANCILLARY RECORD 1

IRG

• EOF

• ANCILLARY RECORD D EOF

DATA SET 2 IMAGE RECORD 1

IRG

•

• IMAGE RECORD G EOF

EOF

• TRAILER RECORD 1

• IRG

•

• TRAILER RECORD P EOF

DATA SET K

• HEADER RECORDS

•

EOF \ ANNOTATION RECORDS

EOF

•• \

ANCILLARY RECORDS

EOF

IMAGE RECORDS

EOF

TRAILER RECORDS

1 FILE

SCENE ONE

FOR FRAMED

BIL& BIP

BAND ONE

FOR BSQ

1 FILE

1 FILE

1 FILE SCENE M

FOR FRAMED

OIL 8i BIP BAND M

FOR BSQ

1 FILE

32
FIGURE 4.

THREE ARRANGEMENTS OF MSS IMAGE DATA

BSQ

(A SCENE OF l\l LINES)

BIL (ASCENE OF N LINES)

RECORD 0

RECORD N-1

LINE 1, BAND 1 LINE 2, BAND 1 LINE 3, BAND 1

LINE N. BAND 1

RECORD 0

1 BAND

OF 1 SCENE

RECORD (n N)-1

LINE 1, BAND 1 LINE 1,BAND 2 LINE 1, BAND 3

LINE

• l.BAND n LINE 2, BAND 1 LINE 2, BAND 2

•

LINE

•

2, BAND n

••

LINE

•

•

• N, BAND n

1 SCENE

(FOR n BANDS)

BIP (A SCENE OF N LINES OF Z PIXELS FOR EACH BAND)

RECORD 0 LINE 1; PIXEL 1 OF BANDS 1 THRU n .. ..PIXELzOF BANDS1 THRU n LINE 2; LINE 3; •

•

RECORD N-1

•

• LINE N; PIXEL 1 OF BANDS 1 THRU n .. .. PIXELzOF BANDSITHRUn

1 SCENE

(FOR n BANDS)

33
and the Canada Center for Remote Sensing late this year. This format however will be

limited to the Band Interleaved by Line (BIL) format of Landsat MSS image data and will

not contain several features such as the record number field, record type code, and the

spare data field as shown in Figure 1. This specific format is described in the following two documents : 1) "Format Specifications for Canadian Landsat MSS System Corrected

Computer Compatible Tape", Research Report 75-3, dated July 1975, by the Canada Center

for Remote Sensing, Department of Energy, Mines, and Resources, Ottawa; and, 2) "Format

Description of the U.S. Landsat MSS Universal Computer Tape", by Lottie E. Brown, GSFC

Document X-563-76-40, June 1976, (to be released July 1976).

34
IMAGE CONTENT AND STRUCTURE

Chairpersons: Azriel Rosenfeld, University of Maryland

Russell A. Kirsch, Applied Mathematics Division, NBS

Panelists: M. A. Fischler, Lockheed Research Lab

K. S. Fu, Purdue University

J. O'Callaghan, CSIRO, Australia

R. F. Sproull, Xerox Corp.

35

Image Content and Structure

Chairpersons: Azriel Rosenfeld and Russell A. Kirsch

To make images maximally useful for interchange, it is necessary for the provider of the

data to furnish descriptions of the image content. At the simplest level such descriptions

refer to the image as a whole, but most interesting and important image sources require articulated descriptions of the image structure giving the relations of the parts of the image

as well as their names. Such image descriptions are useful not only for image data base

management, but also as standards for evaluating the success of pattern recognition

algorithms

.

One current research problem is the development of suitable languages in which to

embed descriptions of image content and structure. This session of the Workshop was devoted

to a discussion of the status of research and related work in graphic languages to provide

the needed basis for image content and structure description.

The panelists were Dr. Martin A. Fischler from Lockheed Research Laboratories, Professor K. S. Fu from Purdue University, Dr. John O'Callaghan from Commonwealth Scientific

and Industrial Research Organization in Australia, and Dr. Robert F. Sproull from Xerox

Corporation. The Chairpersons of the session were Professor Azriel Rosenfeld from the

University of Maryland and Russell A. Kirsch from the National Bureau of Standards.

Rosenfeld began the session with a set of cautionary remarks concerning the difficulty

of image description. At the first level, descriptions can be associated with whole images;

an example of such a description would be the kind of header information included in the

tape formats that were described in the previous session, but the typical header would

not include subject matter associated with the content of the image. Furthermore, the

content can include anything, and may have various interpretations depending on who the

user is for an image. At a somewhat deeper level of description, images can be described

in terms of the names of their parts; map overlays are a good example. But here we confront

the problem that noise for one purpose may be signal for another. Furthermore, the parts

of objects in real world images are very often fuzzy with respect to their boundaries.

Finally, specialized descriptions can be very subtle, and they can be nonsyntactic , referring

to extra-pictorial information. The very general purpose descriptions, for example, "to

37
the left of," can be very difficult to implement in recognizers. (Winston at MIT discussed

this example in his Ph.D. thesis).

Dr. Fischler then discussed scene description for images of real scenes. After distinguishing scenes from their images he discussed the possibility of describing scenes

with picture description languages. Since a picture description language describes a

single image and does not describe all aspects of a real scene, for example, hidden ob.iects

and hidden relations, it seems difficult to see how a picture description language can

be used to describe a real scene. Furthermore, insofar as a picture description language

is symbolic, there are many implied relations in the real scene which cannot be represented

in the symbols unless the symbols themselves are isomorphic with the real scene. He then

discussed certain psychophysical visual illusions, and argued that image perception includes

the consequences of visual illusions which must also be comprehended within image description. He suggested that although symbols might be difficult to use in describing pictures,

the pictures themselves or pictorial counterparts such as overlays might serve such a purpose.

In the discussion Dr. Sproull pointed out that one should be concerned with either describing pictorial scenes or psychological artifacts, not both simultaneously, and that the

problem of describing scenes can be separated from that of describing the perceptual processes for viewing them. Dr. O'Callaghan suggested that once the context for describing

an image is given, this simplifies the problem of one image being many things to many

people.

In Professor Fu's discussion he directed his attention to hierarchical languages.

A description that might be good for characterizing the structure of an image might not

be good for classification purposes. He showed tree and graph structures for describing

images and gave examples for both earth satellite imagery and for fingerprint classification images. He showed how a fingerprint may be partitioned into disjoint regions which can

themselves be easily classified, although this partitioning does not correspond to that used

by fingerprint experts. He showed a similar graph structure for describing a LANDSAT

picture classified for land use purposes.

In Dr. O'Callaghan's remarks he agreed that descriptions are relative to the context

and purpose that they serve. However, for certain pictures, we can sufficiently describe

the context and purpose to enable structural descriptions to be furnished. Such structural

descriptions can be based on objects, relations, and their attributes. Although such

38
descriptions can be adduced from images, the descriptions cannot necessarily be used to

direct automatic analysis or synthesis of images. Dr. O'Callaghan made the important

distinction between picture description languages which can be processed by computer

mechanisms and those which need not have computer implementations if they are to serve

as the basis for human interchange. He exhibited a solution to the problem of image content

and structure description in the application of wood morphology, in which he exhibited

the microstructure of wood tissue in terms of the constituent parts and their relations.

In the discussion David Milgram commented that there is a conflict between Professor Fu's

computer languages and 0' Cal laghan ' s human languages, one presuming the possibility of computer implementation and the other making no such presumption. Fischler agreed that there

need not be a single universal language ranging over different subject matters and ranging

from computers to people. John Dehne concurred that there are perhaps two kinds of languages, those for describing a class of images and those for describing a particular one.

Dr. Sproull began his remarks by pointing out that some people believe if you can

understand something than you can synthesize it. This is somewhat the approach taken in

the computer graphics field with respect to image synthesis. However, in computer graphics,

the effects depend more on iconic properties than they do on realism. He gave a brief

survey of computer graphics activites in the areas of modeling and description, which are

mostly concerned with geometric and topological properties, and hardly with photometry;

in the area of image synthesis, which has dealt primarily with such problems as hidden

line and hidden surface removal; the area of synthesis and analysis, in which the key to

analysis of images is a comparison at higher levels than the individual pixels of image

pairs; and finally, the area of representation, in which the different types of data

structures that serve for computer graphics purposes were described. He summarized the

problem of structural characterization as the problem of deciding what questions should

be asked of an image. Once such a decision is made, the problem of structural characterization becomes simplified.

Kirsch concluded the session with some remarks concerning description of actual scenes.

He suggested that very few serious attempts have been made at describing real scenes.

Most such attempts have been concerned primarily with artificial scenes as in the computer

graphics area. 0' Cal 1 aghan ' s example for wood morphology would be one of the rare instances

in which a successful attempt has been made. With respect to mechanisms for picture

39
description he offered a caveat to SproulTs proposal that programming languages be used

since programming languages are sufficiently powerful that structural descriptions embedded

- in them need not lead easily to analysis procedures. He suggested that Fischler's difficulty in including psychological properties in image description goes beyond the purposes

the image description should serve. He concluded by suggesting that the question of

whether image content and structure can be described in suitable artificial languages is still an open question largely through lack of serious large scale attempts to deal with

the question.

40
ON COMMUNICATING ABOUT PICTURES

Martin A. Lockheed Palo Alto

This session of the workshop is con- cerned with evaluating the prospects for

finding a suitable Picture Description

Language (PDL) to facilitate communication among Image Pattern Recognition

(IPR) researchers. It is therefore

appropriate to ask whether such a language is necessary, and what specific

functions it could serve. Specification of Classification Rules

The most obvious use for such a language would be to specify classification rules for the imagery being processed. However, a representational

scheme general and powerful enoiigh to

effectively satisfy this objective would

probably also provide a solution for most

of the remaining IPR problems (a rather

ambitious goal) . If such a scheme lacked

the required generality, it would hinder

rather than aid communication. While the near term prospects for finding such a language do not look promising, it is

still useful to consider what characteristics such a language must have

.

Description For Scene Reconstruction

A second possible use for a PDL is

related to the fact that in most current

IPR work (especially by workers having a "syntactic" orientation), no distinction

is made between a scene and an image

.

Indeed, for line type drawings, there is no difference. However, for real (or

natural) scenes, a single image is not an adequate representation for many purposes

(e.g., for determining whether or not two

images correspond to the same scene).

Fischler

Research Laboratory

Not only does a real scene have a 3-dimensional aspect which cannot be

captured in a single image, but most

scenes are dynamic entities to some extent

(e.g., leaves on trees appear and disappear

with the seasons, move in the wind, etc.);

further, an image is a function of the sensor, illumination source, transmission

medium, and a host of other factors in

addition to the actual scene content.

If, as indicated above, we cannot

adequately describe a scene with a single

image, then how can an IPR researcher com- municate or record a suitable representation of the subject of his investigations.

Perhaps some PDL, or more likely, a combination of both imagery and linguistic

representation can prove suitable.

Desirable Characteristics of a PDL

Let us now address the question of

the characteristics of a PDL which could

simplify the two scene description tasks

mentioned above (i.e., specification of

classification rules, and description

suitable for scene reconstruction). In

particular, can a purely symbolic language

(i.e., one in which the relationship

between the symbolic tokens and their real

world referents is completely arbitrary)

serve this function? We might note that in

spite of the combined power of natural

English and mathematical notation, we still

find it difficult to communicate about many

subjects without resorting to graphics and

use of pictorial materials . Symbolic

languages pose three problems which I believe makes them (by themselves) unsuitable for general picture description.

41
These are:

a) The need to explicitly enumerate

the various relationships between scene primitives J as well as the need to define

and completely decompose a scene into

these primitives. This is a task which is

not practical for typical natural scenes.

Id) The requirement for the satisfaction of the implicit assumption that

the number of variables needed to describe

a scene is relatively small. Symbolic

techniques do not lend themselves to effective procedures in large number-ofvariables problems. There is no reason

to believe that natural scenes (e.g.,

terrain scenes) can be encoded into

messages containing relatively small

numbers of variables without significant

loss of information.

c) Our lack of knowledge, or inability to describe, the psychological

correlates of a pictorial object. E.G., we are often surprised or amused by

optical illusions, impossible objects, etc. Any representational form which is insensitive to such information will prove defective in applications where human

response to pictorial data is a consideration.

Thus, if it is indeed the case that

we cannot completely replace pictiares with

strings of abstract symbols (and this is

the approach taken in almost all current

research on computer based picture languages), we must see what can be done to use some combination of symbols and pictures, to communicate about pictures » A

combined approach is probably necessary,

since pictures (or picture segments) are limited in their ability to imply scene content which is not explicitly visible,

while as noted above, symbolic description

cannot cope with the undirected complexity

and psychological implications characteristic of natural scenes. Conclusions

In conclusion, I believe that our current state of knowledge about how to characterize natural scenes is inadequate

to anticipate near term standardization

of methods for communication about such

scenes. However, I do believe that it

may be possible, at this time, to specify

minimal criteria for documenting IPR work

employing such scenes. To the extent that

we restrict our attention to artificial

scenes, special applications, or line

type drawings, standardization of communication using a symbolic PDL may be currently possible; but even here, the value of

such standardization to the IPR community

as a whole is open to serious question.

42
SYNTACTIC APPROACH TO THE DESCRIPTION

OF IMAGE STRUCTURE AND CONTENT

K. S. Fu Purdue University

W. Lafayette, Indiana i»7907

Syntactic approach to the description of image content and structure is discussed.

Image structures can be described in terms of trees and graphs with nodes representing

pattern primitives and subpatterns ; and branches representing relations between subpatterns and primitives. Image content can be described in terms of the features or

attributes (geometric and/or texture measurements) of the primitives and subpatterns.

These feature measurements could be interpreted as the semantic information of primitives

and subpatterns. The information of structure and content can also be used for image data

management (storage and retrieval). The correctness and/or classification of such image

content and structure description can be verified from the grammar generating the tree

or graph structures with the associated semantic conditions. Examples are given to

illustrate this approach.

43
LANGUAGES FOR IMAGE CONTENT AND STRUCTURE

J. F. O'Callaghan

CSIRO Division of Computing Research, Australia

The following statements outline my position:

1. Research in picture interpretation and graphics has developed various

languages and representations for images (e.g. tree, array, plex, web

grammars). Such representations are predicated on structural frameworks

of objects, their parts, relations and attributes. An image may be

described by presenting the representation at a certain level of detail or by relating one representation to another,

2. Most research efforts have been confined to restricted classes of line

drawings, using well-defined geometrical relations. It has proved difficult

to computationally define descriptions which are meaningful for humans and

which account for 'real-world' data. One problem is the ambiguous nature

of the data amongst different observers.

3. Attempts to automatically extract (or synthesize) descriptions for given

images have largely failed. A major reason has been the problem-solving

nature of the extraction process, requiring information not usually supplied

with structural representations.

4. Given the status of research, descriptions of images for interchange must be

considered within restricted domains and purposes (e.g. chest x-rays for

demonstration of lung cancer), where a context for term definitions could be

unambiguously established. It would not be possible in general to automatically extract descriptions of content and structure from image data.

However, it is conceivable that such descriptions could be transported and

interrogated as a data base in a restricted dialogue.

44
Constructive Descriptions of Images

Robert F. SprouU

Xerox Palo Alto Research Center

A common method for documenting the structure

of an object is to document the process of constructing it from a small set of primitive

ingredients. Such structural synthesis of images is common in computer graphics, which has

concentrated since inception on generation rather

than analysis of images. This note recounts computer graphics techniques applicable to describing images but does not offer any new

solutions.

The constructive descriptions used in graphics are

often abstract models of a real situation: images

generated from the models are intended to communicate information to a human who

interprets the abstraction. For example, a computer model of an electronic circuit is often

displayed using conventional abstract symbology of

electrical engineering. The model may also contain

additional information adequate for calculating the

\ physical placement of components, the circuit response, or the manufacturing cost. The design of

i the descriptive model is governed by the

I information it must represent, that is, by the

j questions that are asked of the model.

We will survey two specific sorts of models that

I may be suggestive of ways to model the structure

j of some images. First, we can attempt to model

i physical objects and the processes by which images

i of these objects are made on the retina. The

j model, or information derived from it, yields

j information about the structure of the image.

Second, we can use abstract graphics models to describe the image itself, independent of the

; processes that generated it.

Three-Dimensional Geometric Models

Techniques have emerged from computer graphics

research for modeling certain three-dimensional

j objects and for producing somewhat realistic images of scenes composed of these objects. The

geometric models of objects commonly use

I polyhedra, parametric polynomial surfaces and

j conic surfaces as primitive elements [11, 1].

1 Increasingly, computational geometric techniques

seek to model complex objects or complex

I assemblies of objects by sequences of simple

I operations applied to primitive objects [2, 4, 8].

Current methods for synthesizing images from

such models are compromises between efficiency

I and proper simulations of imaging [12]. Although

some hidden-surface elimination techniques

correctly solve the geometric problem of determining which surfaces are visible, only crude

illumination models are used to calculate

intensities on the surface. These methods are adequate for creating images intelligible to humans, but fall far short of simulations of reality.

To decide whether a model captures the structure

of a natural image, we can compare the image with one synthesized from the model. The comparison

can be done on the sampled image directly, or , perhaps after some processing such as feature I extraction [6, 3]. Errors detected by the

comparison need to be related to an error in the

model, which may represent a structural fault (e.g., omission of an object), or a less serious difficulty

(e.g., improper illumination of an object). See

| Figure 1. !

The practical application of these techniques is severely limited to a small class of scenes. However, the class increases as humans

increasingly construct physical artifacts that have

geometrical models: the number of bits used to describe the shape of a General Motors automobile

descends as more of its parts are designed by a computer-aided-design system.

The class of images susceptible to synthesis from

models can doubtless be increased by more

research. For example, why is it not possible to build models of X-ray imaging processes and of chest cavities to describe a patient's chest and the

corresponding chest X-ray? The benefits of a physical analog, the phantom patient, are evident -, in training radiographers.

( Two-Dimensional Image Descriptions

If synthesis from a model of the scene is not ' feasible, it may be possible to describe the

structure of the image as a synthesis of regions,

each of which plays a structurally relevant role. We can describe a region by a description of the

region boundaries, together with annotations

which identify aiid describe the region and may

relate it to other regions in the image. Boundary

descriptions can be simple closed geometric

figures (eg, polygons, conic sections) or sej quences of primitive boundary elements (eg, lines

parametric curves, or even points) [10]. It is a simple matter to determine from any of these

descriptions which pixels lie within a region. The I
relations among regions contain valuable

information about the structure of the image. See

[8] for an example of two-dimensional region

descriptions and of relations among the regions.

The region descriptions can be viewed as a set of

"overlays," each of which describes a region of structural interest in the image. The spatial

descriptions and annotations can also be viewed as

a recipe for constructing the image from its structural components.

Representations

Straightforward representations of regions that are readable by both humans and computers are

feasible. Perhaps the basic element of description

is the relation; although the examples use the

Figure 1. An example of comparing a synthetic

image and an image derived from a natural scene. The top image is synthesized from a geometric

model of a water pump and impeller; the bottom

image is derived from a natural image of the

scene. Taken from [3].

notation of Leap [7], the methods of relational

data bases [5] are equally applicable. Relations are expressed as triples that relate three items: A ® 0 = V. This can be read as "A of O is V." An item

may have a datum, consisting of numerical or

string data to be associated with the item. For

example, an item that represents a boundary might

have as datum a string that gives the chain

encoding of the boundary.

The simplified example below shows how one might describe an image consisting of a single

cell. The boundary descriptions for the cell and

nucleus have presumably been traced by an expert.

Appropriate medical findings are also recorded:

; Description of the parts of the image.

PartOf ® Image = Celll

; Description of the cell, and enumeration of its parts.

PartOf ® Celll = Membranel

PartOf ® Celll = Nucleusl

Type ® Celll = RedBloodCell

Pathology (8) Celll = Normal

; Relations between regions and structural parts

(nucleus, cell) Region <8i Celll = Regionl

Region ® Nucleusl = Region2

Within ® Regionl = Region2

; Properties of regions

Boundary ® Regionl = Boundaryl

Boundary ® Region2 = Boundary2

; Coding information for the boundaries of the

regions

CodingType ® Boundaryl = Chain

CodingType ® Boundary2 = Chain

; A chain coding (taken from Freeman)

datum(Boundaryl) = "042600001042700003

22111070067655445442240400"

datum(Boundary2) = "042600003042700003

2210765530400"

This example shows but one of many possible ways

to record information about the structure and

relationships among regions of an image. For

example, we could give a computer program that

will synthesize a similar image, provided the

program itself conveys structural information.

The appeal of descriptions such as these is that

they can be reasonably understood by humans, and,

if properly designed, can be read and processed by

computer. An image can thus carry with it a data

base to which important questions can be

addressed: it can record the analysis of expert

observers of the original image. It should be

feasible with present understanding to design a standard representation for this data base.

46
References

[1] Barnhill, R.E. and Riesenfeld, R.F. (eds),

Computer Aided Geometric Design, Academic Press, 1975.

[2] Baumgart, E.G., "GEOMED -- A Geometric

Editor," Stanford Artificial Intelligence Lab.,

Memo AIM-232, Stanford University, May 1974.

[3] Baumgart, B.C., "Geometric Modeling for Computer Vision," Stanford Artificial Intelligence

Lab., Memo AIM-249, Stanford University, October

1974.

[4] Braid, l.C, "The Synthesis of Solids Bounded

by Many Faces," CACM, vol. 18, no. 4, April 1975.

[5] Codd, E.F., "A Relational Model of Data for Large Shared Data Banks," CACM, vol. 13, no. 6, June 1970.

[6] Falk, G., "Computer Interpretation of Imperfect

Line Data as a Three Dimensional Scene," Stanford

Artificial Intelligence Lab., Memo AIM-132,

Stanford University, August 1970.

[7] Feldman, J.A., and Rovner, P.D., "An

ALGOL-Based Associative Language," CACM, vol.

12, no. 8, August 1969.

[8] Feldman, J. A., and Yakimovsky, Y., "Decision

Theory and Artificial Intelligence: I. A

Semantics-Based Region Analyzer," Artificial Intelligence, vol. 5., p. 349, 1974.

[9] Forrest, A.R., "Computational Geometry -- Achievements and Problems," in [1].

[10] Freeman, H., "Computer Processing of Line

Drawings," Computing Surveys, vol. 6, no. 1, March

1974.

[11] Newman, W.M. and Sproull, R.F., Principles of Interactive Computer Graphics, McGraw-Hill, New

York, 1973.

[12] Sutherland, I.E., Sproull, R.F. and

Schumacker, R.A., "A Characterization of Ten

Hidden-Surface Algorithms," Computing Surveys,

vol. 6, no. 1, March 1974.

47

DOCUMENTATION OF THE RECORDING ENVIRONMENT

Chairpersons: Judith M. S. Prewitt, National Institutes of Health

John M. Evans, Jr., Office of Developmental Automation

and Control Technology, NBS

Panelists: Wayne Huelskoetter , Dicomed Corp.

Kendall M. Preston, Jr., Carnegie Mellon University

James Greenleaf, Mayo Clinic

Werner Frei , University of S. California

J. K. Zieniur, Bureau of Ra.diological Health

Marvin Maxwell, National Aeronautics and Space Administration

Brent Baxter, University of Utah

Joseph Boccia, M.D., U. S. Army

49
1
Documentation of the Recording Environment

Chairpersons: Judith M.S. Prewitt and John M. Fvans, Jr.

The objective of this session was to identify the common problems associated with

the transformation from a physical object to a digitized image. Optics, electronics,

analog-to-digital converters all influence the resulting data and their effects must

be calibrated and documented to insure correct and successful subsequent processing in

image pattern recognition work.

The session was organized as a series of eight presentations by speakers experienced

in and expert in selected diverse and complementary aspects of this set of problems.

The speakers discussed both the generic problems associated with image digitization

and those specific problems associated with medical and satellite imagery applications.

The questions and answers for each topic follow the respective papers of the speakers

This format was adopted to keep the flow and relevance of the discussion in the context

of the papers of each speaker.

CONCLUSIONS : Whereas the problems of geometric distortion, resolution, vignetting,

amplifier and sensor non-linearities, and A/D converter errors are generally understood

and recognized, there are, in general, no commonly used universal calibration test

patterns or techniques to remove these effects from the final digitized image data.

Every investigator makes the attempt to remove the effects of the recording environment,

but there is a need for test patterns and calibration techniques, and for associated

documentation, to provide better uniformity and higher quality in digitized imagery.

Universal test patterns for automatic image pattern recognition would fall into

two classes:

First, for digitizing systems working from film: a checkerboard pattern with sharply

defined grid edges, with stepped and continuous gray scale portions, and with a uniform

gray border to detect vignetting.

Second, for microscopy: a similar pattern deposited on a microscope slide.

There was, in addition, discussion of calibration of ultrasonic pulse and imagery

systems, but no clear consensus appeared on calibration techniques or test objects or

images

.

51
INSTRUMENT PARAMETERS IN IMAGE DIGITIZATION

Wayne R. Huelskoetter

DICOMED Corporation

INTRODUCTION

Defining an image in digital form is a relatively straight forward process--or so it

seems on the surface. The image or picture is simply subdivided into a number of points

commonly referred to as picture elements or "pixels"; each pixel has a numerical number

assigned to it representing the brightness of the image at the pixel point.

However, to extract useful information from the digitized image, it might be helpful

to know a few parameters associated with the digitization process.

DIGITIZING PARAMETERS

Resolution

The first question usually asked relative to a digitized image is "what's the resolution?" The typical answer is "1024 x 1024" or "25 microns". Neither answer, of course,

defines the resolution of the digitized image. In fact, the term "resolution" is one of

the most confusing, misused and misrepresented parameters in digital image processing.

Because of the large number of disciplines involved in the manufacturing and use of digitizing equipment, the variety of terminology of these disciplines compounds the confusion.

The photographic expert feels comfortable with line pairs/mm or microns, while the computer scientist prefers a nice binary number. The electronic engineer may prefer TV lines/

inch, and the optical engineer prefers MTF. Different equipment technologies make it more

convenient to use one term as opposed to another. For example, the resolution of a microdensitometer is easier to describe in terms of aperture width, while with an electronic

scanner using a TV tube, image dissector tube or CRT, it is more convenient to speak in

terms of TV lines/inch.

Rather than launching into a lengthy discussion of the merits and problems associated

with the various terms currently used to define resolution, let's take a look at a set of

parameters to be used to define the resolution characteristics of a digitized image which

can be applied regardless of the scanning or digitizing device employed.

52
1. Scanning matrix: The number of pixels per line and number of lines per

image (i.e., 1024 x 1024, 480 x 1240, etc.)

2. Pixel size and shape: The actual size referenced to the film plane specified in microns. The shape (circular, square or rectangular) and

the intensity distribution should be specified.

3. Pixel spacing: The distance between adjacent pixel and adjacent lines as measured between centers and specified in microns or millimeters.

4. Scan area: The dimensions of the area scanned in millimeters.

Photometries

The most common way of specifying the photometric characteristic of a pixel, and hence

the digitized image, is the number of gray levels; i.e. 64 (6-bit) or 256 (8-bit). While

these numbers are meaningful, they tell only part of the story. It is also necessary to

know if the levels are transmittance or density levels, or some other function of input

codes. Signal to noise (S/N) ratio of the digitizing equipment is also an important consideration. For example, one could hardly expect 256 accurate levels with a signal to

noise ratio of 100 to 1.

If an electronic scanner is used, it may also be necessary to know the uniformity of

the scanning system. This is usually expressed as a percentage. Therefore, to adequately

describe the photometric characteristics of an image, the following information should be

provided.

1. Transmittance or density levels: i.e. 64 or 256

2. Signal to noise ratio (S/N): db

3. Density range of image: i.e. 0.02 - 1.5D

4. Digitizing system uniformity (if applicable)

The digitization of a color image adds another parameter to be concerned about--spectral

response of the sensor, the color filters, light source, etc. In other words, it takes

more than simply digitizing the image three times through red, blue and green filters.

Unless the spectral response of the digitizing system is reasonably equal, relative to the

red, blue and green components, the resulting digitized image will not be an accurate

representation of the image.

Geometri cs

For many applications of digital image processing, particularly those involving measurements, the geometric accuracy of the scanning system is important. Geometric parameters of importance include:

53
1. Orthogonality between X and Y axis: specified in degrees.

2. Rotation of scan lines to film.

3. Scan line curvature: percentage deviation from a straight line.

4. Scan line linearity: percentage deviation from an ideal pixel to pixel spacing.

It is also important to understand how the various parameters are defined and measured

for a particular device. Numbers without definitions are meaningless.

Other Considerations

The parameters of resolution, photometries and geometries as described above will adequately describe an image in most cases. However, some applications may require knowledge

of the stability, repeatability and drift of the digitizing device used. These characteristics become very important when digitizing color images since usually three scans of

the image are required. Any drifting in the spatial positioning or scan position due to

color shift through the filter can be significant.

The speed of the digitizing device is, or course, an important throughput parameter

for the person doing the scanning. However, once the data has been recorded on tape and

sent to another person, the speed has little significance. Therefore, except to the

extent that the scanning speed has a direct effect on S/N (in that case the S/N ratio pertaining to the scan speed should be referenced), it is not a parameter needed to define the

digitized image.

SUMMARY

As we all know, no digitizing device is perfect. Each has advantages and disadvantages, and trade-offs must be made based on the needs of the application. The resultant

digitized image will be distorted to some degree. Therefore, it is important to understand

characteristics of the digitized image in terms understandable by all of the recipients of

that digitized image. The parameters discussed above and summarized below should, in the

majority of cases, define the image independent of the scanning techniques.

1. Resolution 2. Photometries 3. Geometries

a. Scan matrix a.Transmittance or density levels a. Orthogonality

b. Pixel size and shape b. Signal to noise ratio b. Rotation

c. Pixel spacing c. Density range of image c.Scan line curvature

d. Scan area d. System uniformity d.Scan line linearity

e. Spectral response (color)

Just as in the evaluation of music where the quality is determined by the pleasure of

the listener, the quality of the digitized image will be determined by how well it fits the

needs of the user.

54
Discussion

Wayne Huelskoetter

Diecomed Corp.

Instrument Effects in Digitizing an Image

Question: These effects are difficult to measure. Is a universal test image possible?

Answer: Ronchi rulings are now often used. A universal test image may be possible.

Comment: In digitizing photographs using a drum scanner, sudden intensity changes lead

to transient distortions because of inevitable instrument response time constants. This implies a variable signal to noise ratio.

Question: How accurately can you return to the original pixel?

Answer: In electronic systems, after warm up, you can get back pretty well (±1 pixel.)

Question: What do you mean by 25 micron spot size?

Answer: This is a convolution of the instrument function with the source function,

which is a smoothly peaked function. Spot size obviously depends on where

you make your measurements, for example, half width at half maximum.

Question: What about modulation transfer function (MTF)?

Answer: Can use MTF but it doesn't always mean anything. We have tried to use it.

Comment: Resolution and grey scale interact.

Answer: Yes.

Question: One must distinguish between resolution elements and pixels. S/N ratios

are defined only in a resolution element. This is particularly a problem

for opaque material because you can't get opaque scanners. How do you define

total resolution?

Answer: You can use a test image.

Question: How much resolution do you need in the test image?

Answer: That depends on your need. You don't want to overdo it. People don't analyze

their requirements. The best resolution may not imply the best instrument

for a given application. For example, a system with given resolution may

have to run much slower than another to get the same S/N ratio.

55
CALIBRATION OF TELEVISION MICROSCOPES

K. Preston, Jr.

Carnegie-Mellon University

Pittsburgh, PA 15213

1. INTRODUCTION

With the commercialization of clinical

television microscopy as regards automatic

cell analysis and hematology, about 1 billion cell images are digitized and processed

each year in the United States. Proper

calibration is necessary in order to assure

the reliability and repeatability necessary

to produce valid cell identifications in

these machines. In these microscopes image

information is transferred from the object

(a cell) on the microscope slide via the

imaging optics into image space using electromagnetic radiation in the visible portion

of the spectrum. Calibration of this process must include data on colorimetry, resolution, linearity, and dynamic range. It is

the purpose of this paper to outline certain

calibration techniques which are used for

this purpose,

2, SCANNERS -

There are two primary types of television microscope scanners. One is the

image plane scanner; the other, the flyingspot scanner. In the image plane scanner,

the light detector lies in the plane which

is conjugate to the eyes of the viewer.

Such a configuration is easily instrumented

using standard commercial microscopes and

taking advantage of the trinocular tube

which is ordinarily used for photomicrography.

The flying-spot scanner differs from

the image plane scanner in that the illumination source is placed in what is usually

the image plane of the microscope. The microscope optics form an image of this plane

onto the specimen. The size of the source

is kept small (equal to 1 picture element

in diameter) which leads to the name "flyingspot". Light transmitted through the speci- men on the microscope slide is collected by

the microscope condenser optics, which, in

turn, cause it to impinge on the light detector. Flying-spot scanners are easily

implemented using cathode ray tubes placed

in the image plane with the illumination

directed down the trinocular to the specimen.

An excellent survey paper describing such scanners is provided by Mansberg and Ohringer. An. N.Y. Acad. Sci. 157. 5-37 (1969).

Modem scanners (especially commercial

scanners for cytology automation) operate

under control of a digital processor. Also

included is automatic transport of the

microscope slide which, of course, includes

both automatic focusing and the automatic

location of cells on the surface of the

slide itself. The systems use closed-loop

focusing mechanisms which maximize the high

video frequencies in the television signal.

Some systems are capable of maintaining

focus within a few microinches,

3, CALIBRATION OF IMAGE QUALITY

There are four methods of documenting

the recording environment as far as the performance of the scanning mechanisms is concerned: (1) Resolotion calibration, (2)

Color calibration, (3) Calibration of

linearity, (4) Calibration of djmamic

range.

Resolution may be calibrated quantitatively by measuring the modulation transfer

function of the system using either bar or sinewave targets or by producing images of

fine structures such as diatoms. Unfortunately, the modulation transfer function

varies across the field of view and mechanisms such as the contourgraph must be used

to determine system performance across the

entire field of view.

Colorimetric calibration is usually

performed in a global fashion using a prism

or grating monochromator. This device measures the overall spectral response including the spectral output of the light source and the spectral sensitivity of the

photodetector . Such calibration is necessary in order to compensate for varying

levels of photon flux as the illumination

wavelength is changed or as the color of

the specimen changes.

Linearity and dynamic range are usually

calibrated simultaneously by varying the

photon flux through the optical system at fixed colors or over the entire spectrum.

In order to simplify the calibration the

entire spectrum white light is frequently

56
used and the mechanism for varying level is

the so-called "neutral density filter". The

output of the detector is measured as a function of photon flux. At the same time

noise measurements are performed in order to determine the calibration uncertainty. This

latter measurement must be accompanied by a measurement of the bandwidth of the associated video amplifier.

4. CONCLUSIONS

In order that images digitized either

by flying-spot or image plane television

microscope scanners may be validated for use

in experiments in Image enhancement, image

bandwidth reduction, image transmission, and

in image mensuration for the purpose of pattern recognition, a set of standardized

tests should be established which will define the recording environment in as quantitative a manner as possible. The spectral

response of the system should be determined

in a relative manner either using standard

color filters or a monochromater of a standard slit width (expressed in equivalent

nanometers) . This will permit a correction

of the stored image for the spectral sensitivity of the scanning detector as well as

the spectral emissivity of the associated

light source.

In addition it is necessary to calibrate resolution and modulation transfer

function, both of which can be done by the

simple expedient of scanning a knife edge at different parts of the field or by producing

a contourgraph. The Laplace transform or a Fourier analysis of this step-function re- sponse is all that is required to determine

the MTF. Lastly, dynamic range and linearity may be determined using an appropriate

set of neutral density filters.

Periodic calibration is necessary (ideally before and after image digitization) so that the images archived may be standardized. Furthermore it might be opportune to use standard color images over standard

fields of view in order to check noise on the picture element by picture element

value. This, however, is difficult in that

it presents severe alignment problems.

It is hoped that this short description

of calibration problems and some of their

solutions will be found useful in establishing a television microscope image data base

for use in picture processing and pattern

recognition not only for cell images but

also for use in other types of imaging via

the television microscope scanner.

57
Discussion

Ken Preston

Carnegie Mellon University

Calibration in TV Microscopy

Question: What can be done to achieve selective collection of data?

Answer: That depends on the example. For SEOS only 1% of the data is analyzed.

This is an excellent example of the problem. However, no one feels burdened

by this unused data. On the other hand, blood cell analysis is good: the

microscope looks at millions of cells and outputs just five lines of print.

Comment: You need to know a priori what you're looking for to select out desired data.

Question: In measuring linearity, do you want geometric or gray scale pattern?

Answer: Gray scale. A checkerboard can do both.

Question: In calibrating these instruments, there is a need for protecting the public.

Should the National Bureau of Standards do this?

Answer: The Federal Drug Administration has this responsibility under the Medical

Instruments Act.

Question: Do you need color in the checkerboard test image?

Answer: No, a gray scale can be used with color filters.

Comment: A physical object is needed as a test pattern, not just a specification of line

pairs/rrm.

58
DIGITIZATION OF ULTRASONIC SIGNALS FOR BIOMEDICAL INVESTIGATION

J. F. Greenleaf, Ph.D.

Biophysical Sciences Unit, Mayo Foundation, Rochester, Minnesota 55901

Transmission images obtained using x-ray energy are analogous to

shooting particles through a mass of tissue and measuring the number of

particles emerging on the other side. Acoustic energy, on the other hand,

can be considered to be a mechanical disturbance of the material itself

propagating through the entire body under investigation. Since the mode of

propagation is the material itself, acoustic energy interacts very intimately with the material properties of the tissue being imaged. Such

strong interactions can sometimes reveal subtle alterations in tissue

characteristics (1,2).

We have recently developed new techniques of imaging with acoustic

energy which when applied under highly controlled conditions can obtain

images representing the two-dimensional distribution of specific acoustic

material properties within tissue. These methods are especially applicable

to imaging of the material properties within breasts. Acquisition, analysis, image display , and processing of resulting images from such data

requires standardization of the entire sequence of processes.

My remarks here will concern only the environment within which the

data are collected and not object descriptions or classification. Acoustic

images obtained from digitized signals consist of values derived from

analysis of, or operations on, digitized signals obtained from acoustic

transducers scanned spatially over known positions on a boundary around

some portion of the object of interest. Elements of the set of activities

required to obtain data necessary for acoustic imaging are: 1) acquisition

of the fundamental acoustic signals, 2) digitization of the signal,

3) measurement of information concerning the transducer characteristics,

motion, and associated lenses, and 4) performance of the analysis techniques utilized to map the raw data into the final image or images.

59
Acoustic signals received from transducers represent pressure amplitude

as a function of time. Very often, in addition to the amplitude of the re- ceived signal, one must also know the time at which the transmitter v/as

pulsed. Transmission systems require, in general, arrival time information

and are usually range gated so that only a portion of the signal time is

utilized for subsequent analysis. On the other hand, echo systems can re- quire digitization of the acoustic amplitude signal for a longer duration

of time which v;ill include all of the echoes returning from the region of

interest.

The stored data are the digitized version of the raw signal or some other function of the received amplitude signal which can represent actual

amplitude digitized at a sam.ple rate high enough to include all of the

expected frequencies in the signal or it can include rectified, clipped or thresholded versions of the received signal. The digitized value can also

represent simply the time of arrival of the pulse or the amplitude of the

first pulse received. An example of a system, for acquisition of acoustic

signals from tissue within a sample holder is shown in Figure 1.

Digital

Disc Tape G^ay Level Display

CDC 3500 ^ 288 K Bytes Core strobe Leusy—

,

Pulsar

Receiver

Cor)trol A/D

1)0 1a X

Buffer

Memory

T/R

Switch Sample

Scan

Drive Transducer

I.

*BiomotioT) Transient Recorder Model 8100

\ / Holder

Figure 1 Computer-based system for acquisition, analysis, and display of

ultrasound signals. Tissue samples, held between lucite plates

are scanned with a computer-controlled scanner. Computer triggers

transmit pulse and controls parameters of A/D conversion of

60
received signal (i.e., voltage range, sample rate, number of

samples, etc). Digitized pulses from each position on 120 x 100

point grid are stored on digital magnetic tape for later display

in B-, C-, or A-scan modes on gray level video display. System

is controlled through remote terminal.

In addition to the digitized version of the signal, one must also

record, or understand by convention, information concerning the transducer

position and characteristics . Acoustic signals to be utilized for obtaining

multi-dimensional images very often require extensive information concerning

coordinates of the transducers which were extant during the transmitted

pulse. Not only the three-dimensional position of the transducer but perhaps its orientation in space also may be required. Scaler calibration of

the coordinate system can be included externally, that is, units of millimeters per pulse or internally with the use of ticks or internal fiducial

marks within the image. Included in the required information are the transducer size, frequency response and whatever lenses miay be utilized. Under

certain circumstances, calibration of the voltage-to-pressure conversion

parameters of the transducer miay also be required.

The generation of quantitative images from digitized acoustic signals

requires some form of calibration, either external or internal to the data,

in order to convert the resultant pixel value used within the image to

actual values of material properties within the imaged tissue. External

calibrations may be included on the tape header such as the period of delay

between the transmitter pulse and the digitizing v/indow. Calibrations

internal to the image, scanned at the same time as the tissue under study,

may include acoustic data obtained from materials of known acoustic property which could then be utilized for obtaining conversion tables to

relate the digitized samples to the corresponding tissue properties. An

example of a device for obtaining both spatial calibration and material

property calibration is shown in Figure 2.

61
D-0:#0

Figure 2 Example of tissue holder used for acoustic evaluation of tissue.

Circular chcimbers can be filled with materials of known characteristics and scanned along with the tissue (transverse section

through canine heart shown) . Subsequent analysis can utilize

signals from knov/n materials for calibration of parameters of

acquisition system. Physical dimensions can be determined by

using edges of lucite block for fiducial marks.

ACKNOWLEDGEMENT

These investigations were supported in part by Research Grants HT 42904,

HL 4 664, HL 60, and RR-7 from the National Institutes of Health, U.S.

Public Health Service.

REFERENCES

1. Greenleaf, J. F., F. A. Duck, W. F. Samayoa, and S. A. Johnson:

Ultrasonic data acquisition and processing system for atherosclerotic

tissue characterization. Proceedings Ultrasonic Symposium,

November 11-14, 1974, Milwaukee, Wisconsin, p 738-743.

2. Greenleaf, J. F., S. A. Johnson, W. F. Samayoa, and F. A. Duck:

Algebraic reconstruction of spatial distributions of acoustic

velocities in tissue from their time-of-flight profiles.

Acoustical Holography, 6 (Ed: Newell Booth), Plenum Press,

New York, 1975, pp 71-90.

62
Discussion

James Greenleaf

Mayo Clinic

Images from Digitized Acoustic Signals

Question: What is the quality of calibration in your data library (for excised breasts)?

Answer: Very low. We are just getting data for developing pattern recognition techniques.

Will eventually do in live breasts.

Question: What is your test object for a chromogram?

Answer: Hard saline, blood samples. These give you 0.1% on velocity. Attenuation

is qualitative, not quantitative. Defining resolution is also hard because

of lens geometry, etc. We want to see 1-2 mm objects with v>v^.^.

Question: Do you do any in vivo measurements yet?

Answer: No.

Question: How does the reconstruction effect noise?

Answer: You have a convolution kernel, so you can unscramble the data. Jitter in

arrival time results in noise in the velocity data.

63
A Suggestion for the Calibration of Digitized Imagery

by

Werner Frei

Department of Electrical Engineering

University of Southern California

Los Angeles, California 90007

In recent years, advanced digital

image processing and pattern recognition

techniques have attained a high degree of

sophistication. At the same time, a growing family of electro- optical image digiti- zers have appeared on the market, supplementing the experimental devices

familiar to the researcheres in that field.

Such devices have been naturally adapted

to the particular applications considered,

and their characteristics are just as varied as the particular designs and technologies employed.

Thus, it is an obvious and legitimate

question to ask what a given image data

set precisely represents, especially if

that data set is to become a standard for

a certain research community. Ideally, one v'ould desire a noise-free measure

of some physical aspect of an object or

scene, as a function of geometric coordinates. The measure could reflect for

example the radiant light intensity, the

reflection coefficient or perhaps the x-ray

absorbtion factor, depending upon the application. In addition, the physical

quantity would ideally be band-limited to the Nyquist rate, e. g. , contain no spatial

frequencies above one-half the sampling

frequency. Finally, the sampling pattern

would approximate a two-dimensional array

of airac pulses.

Clearly, such an ideal case is far

from reality, as a number of distortions

and violations of the sampling theorem are most likely to occur in the image acquisition and digitization process. The major

factors obviously depend upon the particular devices used and can be any combination of the following physical limitations:

a) Optical degradations caused by the

front- end imaging system, which

can introduce geometric distorsions, aberrations and vignetting, as well as limit the spatial frequency content of the image.

b) Photographic non-linearities and

film grain noise when a photograph

serves as an intermediate information storage.

c) Aliasing, which occurs when the

image being scanned is insufficiently band-limited.

d) Other spatial frequency degradations

depending upon non-ideal scanning

apertures.

e) Inhomogeneous target areas when

the image is converted by television-type or image dissector

analysers.

f) Non-linear electro- optical transfer

functions and noise.

64
g) Incorrect spectral sensitivities in

the case of color.

h) Inaccuracies of the A/D converters

Unfortunately, the above factors are not

easily tractable, even with the help of

manufacturer specifications, perhaps be- cause they pertain to a variety of fields

and technologies, but also because some

factors depend upon sometimes critical

operator adjustments.

It is felt that a complete specification

of such parameters would require exactI ing standards which may not be practical

in view of the widely diverse systems and

domains of application. Not only is it questionable whether all measurements

can be done on systems in the field but

i it also appears that complete specificaj tions, if available could be just as conI fusing as certain manufacturer's descript

ions.

The alternative proposed here is that

j a series of images scanned on a given

system at one time be accompanied by one

or two test images of well-defined targets.

Such targets can be easily designed to evidence possible geometric and other

spatial degradations (aliasing, resolution,

for example), in addition to a provision

for grey- scale verification. It is perfectly conceivable that such test images

can be used not only to check the data

but also to restore the digital imagery by

well-known image processing techniques.

An excellent example of this practice

is offered by television engineering, which

has been faced with similar concerns for

several decades. It is believed that the

concept could be adopted easily in the present field, because it provides an objective frame of reference at a very small

cost and without the prerequisite of a very detailed knowledge of the physics

and technologies relevant to the hardware

configurations involved.

65
Discussion

Werner Frei

University of Southern California

A Suggestion for Calibration of Digital Imagery

Question: How about direct digitization of video tape or radio or TV signals?

Answer: You can start at the amplifier. There are electronic test signals for calibraQuestion: Would you clarify the requirement for 4 extra bits for 10% accuracy?

Answer: I can send you the reference.

Comment: You need at least 2 extra bits.

Question: Why can't you make the DC knob disappear by setting the zero level?

Answer: You can. You can avoid all of these problems if you are careful. It is

tion from this point.

careless operators that make problems.

For example, consider these problems as histograms:

Number of

recorded points

Gain too low

Intensity

Number of

recorded points —

Intensity

Gain too high

(cl ipped)

Number of

recorded points

DC setting

Intensity

66
Factors Which Influence Acoustic Images of Medical Objects

J. K. Zieniuk

Institute of Fundamental Technical Problems

Polish Academy of Sciences, Warsaw, Poland

Visiting Scientist at Bureau of Radiological Health

Rockville, Maryland 20852

The ultimate goal of ultrasonic imaging is to obtain a two dimensional representation

of the anatomical region of interest containing as much information as possible. Many factors

influence this image. Because this is a very broad and complicated topic, I will confine the

discussion mainly to the physical factors.

Obviously any means of storing or manipulating the ultrasonic image should minimize the

loss of pertinent information. I shall try to suggest factors relevant to this.

It is these factors that will be important in documentation of an imaging system. Any

ultrasonic visualization system uses a transducer which emits a highly coherent beam of

ultrasonic energy. Because of this high degree of coherence any discussion of such a system

should adopt a Fourier optics approach. One should keep in mind however, the important differences i.e., in the acoustics case because of the longer wave lengths we encounter a different ratio of object dimensions to wave lengths.

In the case of a coherent system using acoustics optic, the limit of resolution is a

function of the F-number of a lens and wavelength, A, of the ultrasound wave. It usually

is defined as 0.61AF, where F is the ratio of focal length to the lens diameter. This value

depends on the phase difference of the two waves emitted by two point sources which are to

I be distinguished. But in any case the value given here is the smallest one. In the case

of a scanning system using a single transducer or an array without any focusing devices the

dimensions of an elementary spot depends mainly on the directional characteristics of the

I transducer(s ) and the distance between the transducer and the receiver. Because of diffraction

i effects which in the case of the coherent wave plays a major role the dimensions of the elementary spot will always be bigger than that obtained with the focusing device.

The amount of information contained in the image depends on the spatial frequency

range of the image. The cutoff frequency is equal to L/2Ad where L is the diameter of the

aperture (square or circle), A is the ultrasonic wavelength and d is the distance between

the aperture and the image plane.

67
During the storage process the image area has to be divided into elementary areas over

which the amplitude is averaged. The sampling theory gives us information as to how large

an elementary area may be and still preserve all information contained in the image. This

theory states that if f^^ is the highest spatial frequency encountered and L is the linear

max dimension of the image, the image should be sampled 4(f^^ L) times. This forces the linear

ma X dimension of the elementary area to be no larger than V2f^^^.

2 Therefore the optimum storage of an ultrasonic image will occur if we have ^(f^g^L)

sample points. Since the dynamic range of the amplitudes in the case of medical applications

can extend over a 100 dB range, it is important that the mode of display, storage or sampling

device have as large a dynamic range as possible to avoid losing the information. In our

discussion we have not yet taken into consideration the measurement time. For medical uses

it is important to be able to perform investigations under dynamic conditions. Some existing

devices are capable of forming images in 1/100 seconds. Storage devices should be able to

store the above mentioned information in less than 1/100 seconds.

In the preceding discussion we have made the assumption that we wish to store a previously formed image, i.e., that formed on a cathode ray tube. The quality of this stored

image is influenced by characteristics of the receiver as well as the storage devices. In

some cases the receiver may degrade the image more than the storage device. In this situation it is desirable to feed the electronic signals to the receiver in the storage system

in parallel mode.

The number and choice of the parameters which should be stored along with the image

depends upon many factors. In routine work with one particular system it would be enough

to record the intensity of the emitted wave, pulse length and repetition rate as well as

electronic parameters which could be changed during clinical investigations. Also medical

parameters should be recorded. In the case of research and development work many more system

characteristics should be recorded. These should include all parameters influencing spatial

frequency band, and amplitude or intensity distribution over the image surface.

Acknowledgement:

Author wishes to thank Drs. H. Steward and S. Smith for reading the manuscript and valuable

comments

68
Bib! iography:

1. G. Baum, Fundamentals of medical ultrasonography, G.P. Putnam's Sons, New York 1975.

2. K.R. Erikson, F.J. Fry, J. P. Jones, Ultrasound in medicine - a review. IEEE Trans.

SU-21 ,3,144-170 (1974)

3. Final report of AEMB Task Group on ultrasonic imaging diagnostic instrumentation.

April 1975 Technical Report. National Science Foundation.

4. J.W. Goodman, Introduction to Fourier Optics, McGraw-Hill, New York, 1968.

5. S. Gottlieb, M. Viamonte, Diagnostic Ultrasound. American College of Radiology.

6. B.P. Hildebrand, B.B. Brenden, An introduction to acoustical holography.

Plenum, New York 1972.

7. G.W. Stroke, W.E. Kock, Y. Kikuchi, J. Tsujiuchi, Ultrasonic imaging and holography.

Plenum Press New York 1974.

8. P.N.T. Wells, Physical principles of ultrasonic diagnosis. Academic Press London,

New York 1969.

69
Discussion

Jersy Zieniuk

Bureau of Radiological Health

Factors which Influence Acoustic Imagery of Medical Objects

Question: What kind of a test object is used for ultrasonics?

Answer: Tried a test with wires. This worked O.K.

Question: How about velocity calibration?

Answer: We are not looking at a volume, only at a pellicle, so we only have relative

amplitudes to consider.

Question: What kind of range, that is, distance from the object to the lens, do you

have?

Answer: 2 cm.

Comment: Range depends on frequency. 30cm at 3MHZ and, for reflection, can go up to

lOMHZ.

70
Documentation of the Recording Environment

by

M. Ritter and M. S. Maxwell

The panel's topic, "Documentation of the Recording Environment," covers the

range of sources that, in remote sensing from satellites, distort and degrade the

informational content of the original scene. These sources are numerous and highly

variable in their effects, and are distributed through all phases of remote sensing

from the initial generation of scene radiances with problem areas such as scene

"noise" due to variable canopy cover and bireflectance properties in the case of

agricultural products to data digitization, detector linearity and noise.

In a rough way, we may distribute the elements of satellite remote sensing into

several general areas:

Physical (Phenomenology)

Scene generation

Atmospheric propagation

Acquisition

Sensor/scanning

Signal Processing

Prefiltering

Sampl ing

A/D Conversion

Digitization

Data Storage and Handling

Communication Links

Tape Characteristics

Reformatting

Interpolation Processes

Information Extraction

I shall discuss briefly two areas that impact strongly on the remote sensing

chain. The first is compensation for the sensor system response, which is relatively

easy to obtain, and to document. The second is the impact of the atmosphere on remote sensing, which is a more intractable problem.

Any sensor degrades the radiance pattern (and hence, the information content) of

the scene under observation in many ways. Diffraction and aberrations of the optical

train spread the energy from each point of the scene into areas in the image plane.

The size of the detector elements and the smear that results from scanning the scene across the detectors further spreads the image. This situation is depicted in figure

1. The scene radiance pattern, for simplicity is taken as a vertical square wave bar

pattern, (a). The discrete distribution of points in the image plane, (b), is the

71
result of the finite number of ray tracings from a single point in the field. Counting

the number of points per unit interval in a given direction yields a point spread

function for that direction, (c). The interaction of the spread function with the scene radiance readily (in this case of a repetitive pattern) exhibits its dependence

on the spatial frequency of the radiance distribution, (d), (e). A graph of the

modulation function with spatial frequency is shown in (f). Representative modulation

curves for the detector and scanning smear factor are also shown on this graph. The

composite system modulation transfer function (MTF) for these three factors is the

product of their individual MTF's and is shown in (g). In practice, the system

modulation transfer function is a measured curve and includes the composite effect of

all system factors. An equivalent measure is the composite system point spread

function, (h).

The system point spread function is of direct interest to us for this discussion.

It represents the fractional radiance "poisoning" each pixel gives and receives from

its neighbors. With this detailed knowledge, the sensor data output can be reprocessed

or restored to undue some or much of the radiance poisoning that resulted from the

spread function. The degree of restoration is obviously signal to noise level

dependent. This type of work is currently being carried out by Bendix for NASA on Landsat imagery in an effort to improve crop classification accuracy.

So much for the easy case. Let us turn to the impact of the atmosphere on the

propagation of the scene radiance pattern. We had tacitly implied that there was no atmosphere in the first case, that is, that the scene radiance reached the sensor collector without alteration of spatial and spectral radiometric distributions. In actuality, the situation is complex, highly variable, and under certain conditions of

significant magnitude in its effect on the information content of the scene. Elements

of atmospheric scattering are shown in figure 2 . Absorptive processes, clouds, and

cloud shadowing are not addressed. The specific intensity of the light at the sensor depends not only on the reflectivity of the object under view in the scene but also

the solar illumination at the top of the sensible atmosphere, the scatter attenuation

of this illumination as a function of the incident and emergent optical path lengths

in the atmosphere, the path radiance due to atmospheric scatter, the albedo of the

background and its scatter into the sensor's field of view. Further, the scatter

function depends upon the aerosol content of the atmosphere.

To a first approximation, ignoring inhomogeneity and anisotropy, two known

landmarks or training sites in a total scene would be sufficient to rectify the

alterations due to the atmosphere, viz.

The irradiance at the sensor may be expressed as:

where

is the impingent solar illumination

s the average reflectivity in band

the effective optical path length for band

© is the sensor zenith angle

is the path radiance

It thus follows that: U

72
The . ^<t>> are measured quantities, hence, if the are somehow known,

then /Hq,vis determinable.

The unknown can thus be inferred by:

As of now, no atmospheric corrections are being applied in an operational mode

to satellite imagery. Research in this area is being conducted presently at the

NASA/Johnson Space Center.

73
[ 74
75
Discussion

Question;

Answer:

Question:

Answer:

Comment:

Comment:

Question

Answer:

Milton RitterNASA Goddard Space Fl Ight Center

Sensor and Atmospheric Effects on Satellite Imagery

In inverse filtering, you have a point spread f(T) which isn't constant.

You need at least nominal degradation and can do better. T is a second order

effect.

Do you know the spread function?

You know the design spread function.

This has been done for space images and second order corrections really are second order.

You can do second order corrections "on the fly".

How about S/N?

You can improve by correlation with neighboring points.

76
Correcting Gray Scale Distortions in Photographic Images

by

Brent S. Baxter

Computer Science Department

University of Utah

Salt Lake City, Utah 84112

The objective in digitizing an image is to obtain a numeric representation having

some known relation to the spatial distribution of light energy incident on some optical

surface such as a camera film plane or the retina of the eye. One common relationship

(and indeed a very useful one) is simply to require that the numeric data be proportional

to light intensity. Simple as this seems, the introduction of a photographic process

between the original scene and final numeric data requires a carefully applied

calibration and correction step to correct for gray scale nonl ineari ties introduced

by the film.

To measure and correct for these distortions, a sample of film, identical to the

one used in the camera, is exposed to carefully measured amounts of light in a sensitometer or other suitable instrument. Figure 1 illustrates this situation schematically.

Both films are processed together in the same chemistry and the density of the test

film is measured as shown in Figure 2. Note that the photographic density* is an

approximately logarithmic function of exposure. Note also that substantial modifications

to this function are possible by changing the development process. This is the reason

both films should be processed simultaneously.

77
78
79
Discussion

Brent Baxter

University of Utah

Correcting Gray Scale Distortions in Digitizing Photographs

Question: There is a problem of getting an invertable F.

Answer: If image has very dark or very light regions, you want a low contrast film

to be able to encompass the entire range. You can increase range by cutting

down development time.

Question: What about resolution at high silver regions?

Answer: You must be able to measure Iq accurately.

Comment: Put a wedge on known substance. (Gives known density on film to automatically

calibrate the densitometer during read out).

80
Discussion

Lt. Boccia

Armed Forces Institute for Pathology

The Recording Environment in Pathology

Question: Would you comment on the data bank of images?

Answer: This is proposed. We are just beginning. We have the instrument: the scanner,

l/2y stepping stage, and a small minicomputer (16 bit, 32K core, 20M bytes disc)

It takes 2 minutes to scan a cell, but this is very flexible. The system looks

for a nucleus, can edit data, and can get skeleton overlay.

Question: How do you do calibration?

Answer: We will need test patterns. Dr. Bahr and electronics staff will do it.

Question: Would you be willing to reblock your data to another standard format?

Answer: Yes.

81

PROTOTYPE IMAGES

Chairpersons: Theo. Pavlidis, Princeton University

Roger N. Nagel , National Institutes of Health

Panelists: Jack Sklansky, University of California, Irvine

Sam Dwyer, University of Missouri

Fred Billingsley, Jet Propulsion Lab

83
jiI\a

l:
Panel on Prototype Images: Overview

by

Roger Nagel

The panel on prototype images was formed to consider the goals and selection

criterion for a centralized data base of prototype images. The panel addressed the topic

from several points of view including the philosophy, practicality, and utility of

such an endeavor.

In the first three of the panelists' presentations, the design goals, practical

problems, and technical problems attended with the formulation of an image data base

were discussed. These talks as summarized by the panelists themselves are presented in

the text. The remaining two panelists described their experiences in the actual

preparation and use of data bases designed for specific application areas. Again these

presentations are presented in this report.

After the individual talks and at the conclusion of the presentation audience

participation was solicited. While no consensus can be claimed there was agreement on

several issues as summarized below:

On the topic of should a data base of limited size be solicited and distributed

by a central facility, the general sentiment was yes, and the suggestion that NTIS or

NBS spearhead the effort was made.

Two competing goals for such a data base seemed to divide the audience. Some see

: the goal as facilitating the comparison of algorithmic techniques, i.e., texture measures,

while other feel that specific application areas such as radiography should be the main

selection goal. It was apparent to all that no single data base would suffice, and

that the creation of several data bases by parties with similar goals should be encouraged.

I It was pointed out that several such data bases currently exist. Equally obvious is the

desire that all such data bases be in some "universal" format hopefully agreed on or

specified by this conference.

No matter what the goal of a candidate data base, there seemed to be agreement that

selection criterion and screening of data base entries were important. It was suggested

I that in formulating a candidate data base a committee be appointed to screen and solicit

85
images. Criteria should include but not be limited to images with varying degree of

difficulty, prior results on published algorithms, reasonable sample sizes, and precise

documentation on the source and recording environment of the imagery.

It was further pointed out that the availability of prototype imagery is often

complicated by legal problems as in the case of medical data, and classification

problems as in the case of intelligence data. In addition it is frequently difficult

to document the "ground truth" of the image as human photointerpreters , experts in the

various fields, often disagree.

Finally, the problems of differing image size, bits per pixel and pixel data types

represent an additional source of problems in a centralized data base. Images as small

as 256 pixels to those with millions of pixels are used while individual pixels can be

binary or complex valued.

In summary, it was felt that the problems to be faced in a centralized image data

base are formidable, but that practical data bases related to specific goals could be

solicited, selected, and distributed if well defined goals and selection criteria

are employed.

86
PROTOTYPE IMAGES - GENERAL REMARKS

by

Theodosios Pavlidis

Dept. of Electrical Engineering & Computer Science

Princeton University

Princeton, N.J. 08540

The major motivation for the

creation of a data base of Prototype Images is that it will allow

the direct comparison and evaluation of various methods in image

processing, pattern recognition,

automated diagnosis etc. Although

pictorial data bases should meet

certain standards of documentation

and portability, Prototype Images

must satisfy a number of additional

constraints. The purpose of this

session i§ to make suggestions and

recommendations in this context. I believe that a number of considerations must be taken into account.

I. Purpose : There are two

major types of work where such a data base will be used, each type

j having different requirements

! (1) Testing of Picture Process-

[ ing Algorithms (Segmentation,

! Boundary Tracing etc.).

(2) Testing of Classification

I Algorithms

.

I In the second case one will

1 deal with specific applications (ejg.

tumor detection in chest radiographs).

In the former there is no such

I restriction and a mixture of sub-

! jects is desirable in order to test

I the generality of a proposed

1. algorithm. In order to offer

I statistically valid results the

' data bases used for the second purpose must be quite large.

^ II. Major Features : (1) It is

j important that the test images have

1 an appropriate degree of difficulty

in order to make comparisons meaningful. (2) The images should have

no bias in favor of any given

methodology. For example some edge

detectors do quite well along vertical or horizontal directions but

not along others. Thus a proper

test-picture should not show directional preference. (3) Precise

documentation about the recording

environment, tape format and description of the contents must be

provided

.

The first two features are of

special relevance for the selection

of pictures for the testing of processing algorithms. This fact

suggests that the use of surrogate

images should be given serious con- sideration. The third feature is of

particular relevance for the testing

of classification algorithms and a number of very challenging problems

are faced there.

The size of the data base for

picture processing tests will probably be rather small. However the

opposite is true for the other type.

Not only must each set of prototype

images be quite large but a, large

number of them will be necessary in

order to take care of the ever increasing applications where the

methodology of pattern recognition

is applied.

A preliminary effort in establishing guidelines for data bases

of prototype images has been made

by a task force (chaired by this

speaker) within the framework of the

Biomedical Pattern recognition Subcommittee (chaired by Professor J.

Sklansky) of the IEEE Computer

Society Machine Intelligence and

Pattern Analysis Technical Committee

(chaired by Professor K. S. Fu). The

task force concluded that it would

be very difficult to establish

precise guidelines for acceptability

of prototype images. Instead it

87
suggested that a review of a proposed data base by three independent

referees be performed. The referees

would evaluate whether it is appropriate to serve as a set of prototype images. Thus standards of

quality would be maintained in a similar manner as for published

papers

.

However a number of questions

remain open: the choice of one or

more facilities where data bases

would be submitted or which would

solicit data bases of prototype

images ; the selection of referees;

the financing of the reviewing

procedure, which can be quite expensive; and the distribution mechanism

It should be noted that these questions are also in the domain of the

IEEE - MIPA Subcommittee on Data

Bases (chaired by Dr. J. B. McFerrani

88
' Prototype Images - Selection Problems

by

j Roger Nagel

The formulation of an image data base raises several important con bent and procedural

questions. At this juncture it is not at all clear how the procedural questions of

i format and documentation can be settled. However, given that such questions can be

! reasonably answered (by other groups at the meeting) we are left with the problems of data

I base collection and distribution.

Implicit in a data base is some notion of content. Yet here again a question of

! scope arises. For example, to what audience will the data base be addressed? If it is

I assumed that the data base is intended for a comparison of algorithms in the techniqueI oriented sense then the ability to cover many application areas is compromised, and vice

versa. This question is essential because of the expected limit to the size of any data

I base in order to make distribution feasible.

In Table 1 a representative list of application areas is presented and in Table 2

a sample listing of techniques is presented. Both the size and incompleteness of these

tables brings home the point that an exhaustive inclusion of sample imagery is not

I practical. Thus the selection of prototype images presents a difficult problem simply

: in terms of content criteria.

I Due to the limited size of a realistic data base, and the obvious fact that it is

I impractical to cover all possible areas, the task for which the data base is intended

becomes a critical factor in the selection of prototype images. If we direct the

j data base to the research and development community, it implies a technique orientation.

Whereas if it is directed at the industrial users, application areas are the guiding

criteria for content selection. My own bias would be toward research and development,

; and I would choose those techniques which are currently in the literature for criteria

} in sample image selection.

!' A reasonable rationale for such a choice is the flow of techniques from the R&D

I community to industry. Thus we provide the test bed for algorithm development and

1 comparison, while the general utility in particular application areas is not yet tested.

89
Even when the content selection criteria are known, we are still left with the

question of mechanism for collection, and distribution. That is, how much screening

of candidate images should be done for documentation, format, prior results, and utility?

Can or should such a data base be updated to grow in number of samples? Furthermore,

should results of new algorithms be added to the documentation, as the data base is used?

In closing I put forward the proposal that a limited data base can be selected and

distributed. In the beginning, a modest set of techniques should be selected as the

basis for content selection. This proposal assumes that some form of universal format

will be developed by the other workshops, as well as a minimum documentation standard.

The distribution of such a data base with no updating facility is now possibly at NTIS,

and other sources.

While such a proposal is only a first step, it is within the realm of possibility

and can be spearheaded by the National Bureau of Standards.

90
TABLE I: APPLICATION AREAS

SATELLITE

RESOURCE MONITORING

WEATHER FORECASTING

MILITARY - TARGET RECOGNITION

INTELLIGENCE RECONAISSENCE

MEDICAL

RADIOGRAPH

RECONSTRUCTION

CYTOLOGY

FORENSIC

FINGERPRINT

HANDlvTlITING

FACE IDENTIFICATION

INDUSTRIAL

NON DESTRUCTIVE TESTING

CHARACTER & PRINT READING

ASSEMBLY LINE MONITORING

ROBOTICS

CARTOGRAPHY

TABLE 2: TECHNIQUE AREAS

POINT OPERATIONS

NEIGHBORHOOD OPERATIONS

TEXTURE

EDGE & LINE

TRANSFORMATIONS

REGISTRATION & SD-IPLE COMBINATION

GEOMETRICAL

SEGMENTATION, TRACKING

QUANTITATIVE MEASUREMENTS

CLASSIFICATION

91
Prototype Radiographs

by

Jack Sklansky

School of Engineering

University of California

Irvine, California 92717

I. INTRODUCTION

A technology of computer-aided image analysis currently is being developed for two

major areas: a) remotely sensed terrestial images, and b) biomedical images. Within

biomedical images, radiographs are receiving special attention. This can perhaps be

explained by the following observations:

1) In the United States, approximately 700,000,000 medical radiographs are

analyzed annually by a population of about 9000 board-certified radiologists.

2) Of these radiographs, about 230,000,000 are chest radiographs.

3) Perhaps a thirty percent increase in the number of radiographs will take place

in response to the concern over the increased costs imposed by malpractice

suits.

4) There is a trend toward more precise and careful documentation of diagnoses

of radiographs also in response to the threat of malpractice suits.

5) The incidence of false negatives in the detection of being tumors is about

thirty percent. Comparable error rates for other lesions seem likely.

6) Among the various forms of images that reveal a patient's internal structures

(e.g., X-radiographs (including xero-grams), thermograms, ultrasonic scans,

and nuclear images), X-radiographs provide the greatest resolution and consequently has the greatest information content per image.

The following are a few of the potential applications for a technology of computeraided analysis of radiographs:

a) routine diagnosis of radiographs, including report-writing,

b) therapy (e.g., radiation therapy; surgery),

c) mass screening

d) public health (e.g., correlating radiographical ly determined structures with

certain populations,

92
e) basic medical science (e.g., relating computer-detected pictorial textures

to diseases)

,

f) training of radiologists.

The development of an image-analysis technology needs standard or prototype

images. In this paper we discuss several of the basic issues involved in the construction

of a data base of prototype radiographs as a means to accelerate the development of an

effective technology of computer-aided radiography.

II. WHY DO WE NEED PROTOTYPE RADIOGRAPHS.

The following are a few of the benefits that would be provided by a readily

available set of prototype radiographs.

1) An objective comparison of computer algorithms developed by different research

groups would be greatly facilitated by a set of prototype radiographs.

2) The acceptance by the medical profession of a computer algorithm for aiding

the diagnosis of radiographs usually implies a change in the routines by which

diagnoses are reached. If a convincing case for such a change can be obtained

at all, it will have to be based in part on a test of the algorithm properly

selected on a set of prototype radiographs whose diagnoses are accepted by

the medical profession as valid.

3) Occasionally a computer algorithm may reveal a relation between computed

pictorial features and otherwise undiscernable physiological or diagnostic

phenomena in the radiograph. To establish the validity and/or reliability of

such a relation will require a set of prototype images illustrating the

phenomenon of interest.

4) For many research groups, a representative set of prototypes will elimijiate or

postpone the purchase, operation, and maintenance of expensive scanning and

digitizing equipment.

One cannot hope to build a set of prototype radiographs in anticipation of all

algorithms that will need to be tested. The domain of possible disease states and

possible physiological phenomena is too large. Hence we must a) construct a small

representative set of radiographs, and b) establish a set of guidelines and criteria

for the selection and solicitation of prototype radiographs.

93
III. EVALUATION CRITERIA

Below we suggest a) the forms that prototype radiographs may take, and b) possible

criteria for determining the acceptability of candidate radiograph for the file of

radiographs.

We suggest that a prototype radiograph may take one or any combination of the

following forms.

a) A "raw" radiograph -- i.e., the most direct highest quality means of storing

the image. X-radiographs are usually recorded on film or xerographic paper.

In cases where an image intensifier tube is coupled to a television display,

the raw radiograph would be a video tape recording. In the cases of computerized

tomography and isotope scans, the most accurate storage technique is a digital

memory -- usually digital magnetic tape.

b) An unfiltered digitized radiograph . This is usually obtained from the raw

radiograph by means of a scanning or reading device and an analog-to-digital

converter, and stored on digital magnetic tape. Computerized tomography and

isotope scans are conveniently stored directly in this form of memory.

c) A filtered digital radiograph . This may be obtained by compressing the

spatial and gray level digitizations of the unfiltered radiograph into a form

(e.g., eight bits per pixel, 256 x 256 pixels per radiograph) that can be

accepted by most minicomputer systems.

IV. DOCUMENTATION OF PROTOTYPE RADIOGRAPHS

Regardless of which of these forms is used, each prototype radiograph will need

to be accompanied by substantial documentation: the raw radiograph requiring the least

documentation, and the digitized filtered radiograph the most.

We suggest that ideally the do'^umentation include the following:

1) Imaging system

Anode voltage

Subject-to-film distance

Anode-to-film distance

Angle of the anode

Anode current

Exposure time

94
Frequency spectrum of emitted X-rays

Dimensions of the Potter-Bucky diaphragm, if used

Types and thicknesses of film, emulsion, and fluorescent screen

Point spread function as a function of position in the image plane

Film processing parameters: especially development time, chemical ingredients,

and temperature.

2) Patient

Weight, height, sex, age, occupation, medical history, genetic factors,

geographic habitat, disease (if known). If a panel of experts established

the disease, the members of the panel should be identified and the distribution

of votes for each polled opinion should be described, without necessarily

revealing how each member voted.

If surgical confirmation of the diagnosis is available, the basis for

this confirmation (i.e., the observation or the pathological test) should

be described.

Portion of patient viewed (e.g., chest, lumbar region, breast)

Projection (e.g., posterior-anterior, lateral)

Geometric parameters (film-to-object distance, size of object, etc.)

3) Phantom of human structures.

A precise identification or description of the phantom should be given. A

full description of the procedure for replicating the phantom should be

available.

4) Test pattern.

Two types of test patterns for prototype radiographs are desirable:

a step wedge , held at a known distance from the film, superimposed on the raw

radiograph; and a three dimensional test object, exposed onto a separate film

without the patient, with exposure and other imaging conditions identical to

that for the raw radiograph.

95
5) An additional documentation for unfiltered digitized radiograph. i

If the digitization was obtained from a scanning device, the basic physical

parameters of the scanner must be documented in addition to the documentation

required for raw radiographs. These parameters include the aperture, the signal- [

to-noise level, the range of linearity of the scanner pixels, and the number

of bits per pixel. If the digitization is obtained from computerized tomography,

the basic physical parameters of the tomographic system (such as the diameter I

of the ray, the distance between adjacent ray positions, and the angle

between adjacent projections) as well as the reconstruction algorithm must

be specified.

6) Additional documentation for a filtered digitized radiograph.

I;' this form, the parameters of the digital filter must be documented, in

addition to the documentation needed for the raw radiograph and unfil tared

digitized radiograph. For example, if the filter operates on a histogram, the

histogram transformation must be described. The number of bits per pixel

and the number of pixels per unit length must be specified for both the

unfiltered and the filtered digitized radiographs.

In addition, of course, the recording parameters must be specified. For

details on this subject see report of session on "Standard Tape Formats."

I

IV. SOLICITATION FOR A CENTRAL FILE

The prototyper to be solicited depends on the types of algorithms under development.

Currently we are aware of research on computerized analysis of radiographs of the following

structures.

1) Chest ;

a) ribs

b) lung tissue (especially pneumoconiosis)

c) lung tumors

d) calcifications

e) heart

f) pulmonary blood vessels

96
2) Breast

j a) cysts

I b) ducts

c) chest walls

d) skin profile

e) carcinomas

3) Bone

a) trabeculae

b) slight fractures

c) spine

d) knee

i 4) Stomach

I 5) Head

' In addition to prototype radiographs of human subjects (e.g., chest radiographs,

xeromammograms , etc.), it will also be useful to have access to prototype radiographs of

j excised tissue. Examples of such tissue are cancerous breaiit tumors, benign brea^st tumors,

diseased bone, and diseased lung tissue. Radiographs - of these excised tissues will

provide data that is not obscured by images of tissue outside the concerm of the

1 algorithm developer.

In addition to prototype radiographs of human subjects and of excised tissue,

it will be useful to have access to prototype radiographs of well designed phantoms.

These phantoms fall in two classes: test patterns and realistic simulations of human

physiology.

The test patterns are needed in order to provide a precise means of modelling the

X-ray imaging system and the effects of the film and Bucky grid . Since the imaging

process depends on the distribution of the density of the subject in 3-space, as well

as on the distribution of the focal spot, it is likely that a wide variety of threedimensional test patterns will be designed and constructed. Evaluating the ability

of various algorithms to model or reconstruct these test patterns may be a useful early

step in the development of these algorithms.

97
A good example of a phantom that simulates human physiology has been constructed

at the University of California at Irvine under the supervision of Dr. E. N. C. Milne and

the technical assistance of W. Roecke.

The phantom simulates the chest: including the ribs, the lung, the heart,

pulmonary vessels, lung tumors, and calcifications. This phantom yields exceedingly

accurate simulations of chest radiographs, and provides a means for accurate control of

size and placement of simulated tumors, blood vessels, etc. Radiographs of such a

phantom can be much more accurately documented than radiographs of a human subject, because

a) the relative positions of all the objects in the phantom are known with great

accuracy, b) the phantom doesn't move during the exposure, c) the phantom permits the

use of small -focal -spot tubes, with the concomitant high quality images at the expense

of long exposures, and d) the phantom permits repeated exposures to provide original

radiographs for the file of prototypes. (Original radiographs of human subjects

usually must be kept at the originating clinic.)

V. CONCLUDING REMARK

Although the needs for prototypes and the associated documentation are quite

complex, modest beginnings in these directions can be made that would be very helpful

to researchers on computer-aided radiography. Examples of these beginnings are:

1) a set of normal chest radiographs

2) a set of radiographs of chest phantoms

3) a specification for a three-dimensional test pattern.

1 ACKNOWLEDGEMENTS „

The author is indebted to Carolyn Kimme for her suggestions and comments.

The preparation of this report was supported by the National Institute of General ^

Medical Sciences of the U.S. Public Health Service under Grant No. GM-17632. ii

98
Thoughts on Standardization of Parameters for Image Evaluation

Fred C. Billingsley

NASA Headquarters

It may be anticipated that images will be received for image processing and analysis

from a wide variety of sources and with a wide variety of sensors. Since it is desirable

to have image processing algorithms be as universally applicable as possible, they should

be designed, where possible, to be insensitive to the parametric variations of the source

material. Where this is not possible, these variations must be taken into account. We

therefore need to consider what parameters may be defined in common across a suite of image

types

.

CONSIDERATIONS OF SOURCE TYPE

Consider for example the four images in Figure 1, derived from a radiograph, the

Landsat satellite, normal film photography, and a scanning electron microscope. The image

producing characteristic of these sources are quite different, yet similar pattern recognition or other image analysis questions may be asked of each.

We need first to consider the difference between image connotations and image content.

Image connotation, in this sense, is that set of external knowledge, culture, education,

or other si tuational ly dependent information which is used in conjunction with the data

of the image itself. This total set of information is, in effect, a model of the real world

of which the image is a replica, and the problem is to answer questions about the real

world using data from the image as one source. Connotation, like beauty, is in the eye

of the beholder. Therefore, if standard images are used as surrogates, a wide variety

must be available so that a suitable one for a given problem may be selected.

To avoid these connotation problems, we need to consider whether it is possible to

define objective parameters or measurements of images which, in the proper combinations,

may serve as surrogates for "real images." These parameters may be pixel-specific, location

dependent, or combinations thereof. Parameters which have proven useful in defining the

characteristics of images include such as the gray scale linearity, granularity of the

quantization, spectral content, geometrical fidelity, resolution of the system expressed

as either the point spread function or the modulation transfer function, and the spatial

frequency content and characteristics of the data itself (which in turn may contain various
amplitudes and bandwidth of noise) or the statistics of the spatial variations of local

areas. In addition, we must recognize the difference between intra-pixel (for example,

the pixel resolution) and inter-pixel (for example, the recognition of multi-pixel objects)

effects.

We will consider here only digital images which are used as fodder for digital processing and will avoid entirely the question of eyeball analysis of visible images, a subject which has had considerable treatment in the literature, although not primarily from

the point of view of selecting standard images. This question of eyeball analysis, however,

cannot be completely avoided since it serves as one interface between the digital analysis

per se' and the human verification of the analysis results.

PARAMETERS

We now need to consider parameters which have some relation to the real world. Although photographic film is quite non-linear certain analyses are only tractable if the

brightness/digital number transfer curve is assumed linear. In addition, the response

of the human eye is approximately logarithmic and therefore comparison of linear digital

analysis with eyeball analysis must be carefully scrutinized. This raises the first question which must be considered in the design of a surrogate image so that the analysis may

relate to the real world image for which the problem is being solved:

Will the true data be recorded on film originally over such a wide range of

film densities that parts of the image will be recorded on the toe and shoulder

of the recording characteristic curve where the local contrast is low? If

so, this must be taken into account when generating the surrogate image.

There seems to be no reason to include gray scales of the standard type in the surrogate

images to be used for digital analysis; however, if included, they will provide some measure

of control of the eventual reproducing process used to display the processed image.

Also, at least for "first order" reference images we will ignore the possibilities

of coma, penumbra effects and the like, thus again opening the possibility of non-surrogate

analysis. We will assume that the system of interest is spatially stationary. In the

same sense, we will define that the system is geometrically stationary, i.e., rubber sheet

distortions internal to the image will be ignored (again, unless specific situations require the reintroduction of this factor).

100
These assumptions will allow us to generate surrogate images with known and predefined

properties instead of requiring (or allowing) us to select images from an external library

and being required to analyze them to determine the properties. Other properties which

cannot so easily be dismissed now need to be considered in some detail.

QUANTIZATION GRANULARITY

The number of gray levels used for quantizing a continuum image into discrete levels

will have definite visible effects even at the five or six bit quantization accuracy point,

and may affect the processing even when more levels are used. This leads us to questions

of the following type:

What is the sensitivity of the processing to a various number of digital

levels? What about truncation in the digital processing? Is there an

implicit level of quantization in the data or the sensor itself? What are the

noise characteristics being simulated? How close a separation of different

gray levels must be detected? Suppose they are adjacent or not adjacent?

NOISE

In the absence of noise, a clean signal will be recorded as the same digital number

every time it is quantized. In the presence of noise, however, there is a finite probability that the digital number assigned to the signal will be different than that which would

have been assigned to the clean signal. This effect is illustrated in Figure 2 and curves

pertaining to two commonly asked questions are shown in Figure 3.

Noise will also perturb other types of processing, especially that which needs uniform

areas or clean edges. This leads to:

In the true scene, what is the variability in nominally uniform areas (in

magnitude and in the special frequency content of the variation)? What

is the sensor noise and its bandwidth? What data questions (see Figure 3)

are being asked? Does noise vary with brightness?

SPECTRAL CONTENT

The spectral content may have as few as one dimension (radiographs, monochrome pictures)

to three dimensions (typical color film) to four (Landsat) to 24 (the NASA multispectral

aircraft scanner). In addition, correlated mul titemporal images may effectively have

even more. Pattern recognition and processing in the multispectral domain is very effective

where available. However, in setting up surrogates for this situation, the covariance

101
between spectral bands which tends to occur, the tendency for the same material to usually

have the same spectral content (in the visible sense, color) and the degree to which given

materials aggregate into more or less uniform areas in the picture must be taken into account. Pattern recognition techniques for use in this situation (e.g., Landsat analyses)

will optimally consider both the tendency of a given material to aggregate in patches in

the image and also to aggregate statistically in multi-dimensional spectral space. Thus

typical questions to be answered might be:

What is the typical multi spectral distribution expected? Are the clusters

spherical or elliptical? i.e., what is the interband correlation? What

is the cluster spread, especially as compared to the typical intercluster

distance? What is the typical aggregation size distribution to be expected

in the image for materials of nominally uniform spectral content? Can any

nonspectral data be mapped congruent to the real image and be treated as

another "spectral band" in a multivariant analysis?

POINT SPREAD (IMPULSE RESPONSE) FUNCTION

Any system upon viewing a delta-function object (for example, a star) will reproduce

that object, not as a delta function, but rather that point spread out in image space around

the ideal delta function location. Since object space may be considered as composed of a

tightly packed array of delta functions each with its own amplitude, the reproduced image

may be considered to be composed of the summation of the corresponding series of point

spread functions (psf) each at its appropriate location and amplitude. This situation

is sketched in Figure 4a.

The basis function used to sample the image at each of the digital locations, combined with the basis function used to reproduce it at each of those locations, will have

a filtering effect on the high spatial frequency content of the image as sketched in Figure

4b. Indeed, the Nyquist criterion requires that this filtering function occur before the

digitization can properly be made. This effect is shown in the spatial frequency domain

in Figure 4d; the upper limit of spatial frequency content after filtering will determine

the appropriate digitization spacing. The effect of this filtering upon a sharp edge is

shown in Figure 4c. It can be shown that the edge softening caused by the filtering, when

combined with the digitization spacing appropriate to that same filtering function, will

result in maximumly obtainable edge transition, from 10% to 90%, of approximately 1.5 pixels.

102
Thus, any generated images representing edge sampling must consider this effect. In particular, those processing algorithms which require sharp transitions for their success

will be particularly affected.

Thus, the type question which we are led to are:

What is the spatial frequency response (or related to that, the point spread

function) of the imaging system simulated? How does the quantization spacing

compare with that required by the Nyquist criterion? What is the distribution

of energy as a function of spatial frequency of the base band data itself?

What reproducing basis function will be used to produce a visible image upon

the completion of the processing?

GEOMETRICAL (AREAL RELATIONSHIPS)

Binary Images

These include that set of images in which only two values of brightness are available,

namely black and white. These images typically take the form of line drawings of specific

objects, of maps (which display the location of objects, but do not represent the objects

themselves), text, including written music (in which written characters of some language

are used to indicate sounds of concepts), homograms (from gram, a display, and homo, uniform,

a display in which norminally uniform or homogeneous subject areas are displayed as uniform

or uniformly textured areas in the image), as half-tone images (in which gray scales are

represented by a spatially varying binary pattern). Figure 5 illustrates some of these.

Here we get to the guts of the pattern recognition problem, and must consider the

questions of the following type in defining our surrogates:

Is the information to be simulated characterized by the properties (e.g., shapes and

sizes) of areas as defined by lines (the edges of the areas)? Or is it in the

areal extent (again, size and shape) of uniform or uniformly textured areas in a

homogram? Or is it the lines themselves as in text or in map? How much culture

(as opposed to information actually in the data itself) is required for the analysis?

Is this apparent in the image or introduced in the subsequent analysis? Is the

analysis of a given area dependent on or independent of the analysis of and/or

interrelationship with its neighbors? Is orientation, size, or scale critical?

Will analysis be by statistics or by template matching? If the former, what are

103
the statistics required for the size and shape of the subelements ; if the

latter, what are the tolerances allowed in the template? Is the analysis

dependent on texture (this includes not only the visible texture, but

perhaps the impression of gray scale evidenced as texture in the half-tone

image)? If the half-tone image, do we handle it binary-wise or do we

reduce resolution and average out the dots and treat it as a gray scale

image? Is edge sharpness important? How about affine transformations,

such as rubber sheet stretching?

With Gray Scale

Again we need to differentiate between information directly within the image and interpretations based on the image used as a data source to allow us to solve an external

model (i.e., the culture factor again). Analysis of information completely contained in

the image might be characterized by (but obviously not limited to) automated clustering,

which reduces the incoming image to homograms. For the gray scale case we must answer

questions of the same type as proposed for the binary case, but in addition, must ask such

questions as:

How shall we handle gradually shaded areas such as produced in images of

cylinders? How do we define or "know" what is represented?

Again we are faced with a culture problem since the "know" is not information in an

image. For machine analysis, this culture (i.e., the model being solved using the image

as a data source) must be defined to the computer in all its painstaking details. Then

for proper simulation the algorithm must exercise all of the expected combinations and must

therefore, include examples of each.

CONCLUSION

Thus, although surrogate images may be generated in terms of quantifiable and definable

parameters such as has been outlined above, the set of parameters actually considered, and

the ranges over which they must be varied, must be defined in terms of the model being

solved. In order to properly exercise and test the analysis, the right questions must

be asked, answered and simulated. Simulation by generated images as outlined here, allows

the control of various parameters which are important and allows the generation of surrogate

images which can be designed to exercise the algoritlifis to their utmost.

104
However, just as a hammer will not turn screws (it is the wrong tool/problem combination), the wrong surrogate/analysis combination will be unproductive at best, or perhaps

downright misleading. In this sense, although the factors outlined above are considered

to be important (but not necessarily exhaustive) in the understanding and definition of the

problem, careful consideration must be given to the entire problem situation to assure

that all factors have been considered.

Fig. 1. Examples of images from widely divergent sources, a) Radiograph, b) Landsat

Multispectral Scanner, c) Film Photography, d) Scanning Electron Microscope.

Clean

Signal

Noisy

Si gnal

rea is probability

of being in first

level above correct

Area is probability

the correct level

Area is probability

of being in first

level below correct

Fig 2. Effects of noise on quantization accuracy (sketch).

105
0.1 1.0 10 p = step size/mns noise

Given a signal uniformly distributed

over the quantization intervals.

Given a Gaussian noise of value =a. The curves show probability of cor- rectly assigning a digital value

corresponding to the noise-free signal

within +0, ±1, ...±9 DN (inclusive)

as a function of the ratio 3 = step

size/o.

Fig. 3. Effects of noise on quantization and on ability to differentiate two areas of

different brightness.

0.1 1.0 K) (3 = Step size/rms noise

Given two signals which have been perturbed

by Gaussian noise of value equal to a. Each is quantized to the same number of

bits. The curves given the probability of

correctly determining the true difference

in the two levels within +0, ±1, ...±4

(inclusive) DN as function of the ratio

B

= step size/a.

INPUT

PSF

OUTPUT

DELTA FUNCTION SIGNAL TRACE EDGE

SIGNAL SIGNAL

OBJECT

MODULATinN;

IMAGE

5MAX; - SMIN.

FREQUENCY

Fig. 4. Filtering effect of the psf a) Effect of finite psf on delta function, b) Effect

on a data trace, c) Effect on edge softening, d) Effect shown in spatial frequency

domain.

106
digili/ing is then foiinil by inlcgr;ili» Iwccii ;ippropii.ilc liiiiils rcprcscnia

bouii'Jarics.

•'

I here is :in inlcrcsiing corolhiry 1( effeels of rcpciil samplint;. C onsider I zero noise, so llial llic signal level is sampled. In lliis ease, llic DN assif preeisely llic saine and it will be in' value of (lie signal from inspecting ll somewhere wilhin ihe range oecupi

oilier hand, if there is even a slight probability that the instantaneous va somewhat, and perhaps widely, diffc will lliereforc be a finite probabiiit

diffcicnt than that of the clean sigi number of samples, and averaging th DN will result which, in its fractional f true signal within the DN range. Doei

Fig. 5. Illustrations of types of images a) Map, b) Line Drawing, c) Homogram, d) Text.

107
WORKSHOP ON STANDARDS FOR IMAGE PATTERN RECOGNITION

June 3-4, 1976

PROGRAM

THURSDAY, JUNE 3, 1976

8:30 AM REGISTRATION

9:15 AM WELCOME TO NBS

Ruth M. Davis, Director

Institute for Computer Sciences and Technology, NBS

9:30 AM INTRODUCTION, HISTORY AND OBJECTIVES OF THE WORKSHOP

John M. Evans, Jr. Office of Developmental Automation and Control Technology, NBS

Russell A. Kirsch

Applied Mathematics Division, NBS

Roger N. Nagel

National Institutes of Health

10:15 AM COFFEE

10:30 AM STANDARD TAPE FORMATS

Chairpersons

John Dehne, Army Night

Army Night Vision Lab

William Alford

Goddard Space Flight Center

Prototype data formats will be presented and di cussed by a mul ti -disci pi i nary

panel. A questionnaire will be distributed for completion by Workshop

participants

Panel ists

Theo. Pavlidis

Princeton University

John Sos

National Aeronautics and Space Administration

Sandra Hawley

ESL Incorporated

12:30 PM LUNCH

1:30 PM IMAGE CONTENT AND STRUCTURE

Chairpersons:

Azriel Rosenfeld

University of Maryland

Russell A. Kirsch

Applied Mathematics Division, NBS

To make images maximally useful for interchange, it is necessary for the provider of the data to furnish descriptions of the image content. At the simplest

level, such descriptions refer to the image as a whole. But most interesting

and important image sources require articulated descriptions of the image

108
structure giving the relations of the parts of the image as well as their names. Such image descriptions are useful not only for image data base management,

, but also as standards for evaluating the success of pattern recognition

algorithms.

A research problem that currently exists is the development of suitable languages

in which to embed descriptions of image content and structure. This session

of the Workshop will be devoted to a discussion of the status of research and

related work in graphic languages to provide the needed basis for image content

and structure description.

Panelists:

M. A. Fischler

; Lockheed Research Lab

K. S. Fu

' Purdue University

i J. O'Callaghan

CSIRO, Australia

j R. F. Sproull

Xerox Corp.

j

i 3:30 PM COFFEE

! 3:45 PM IMAGE CONTENT AND STRUCTURE (CONT.)

I 5:30 PM RECEPTION AND DINNER

FRIDAY, JUNE 4, 1976

9:00 AM DOCUMENTATION OF THE RECORDING ENVIRONMENT

Chairpersons:

! Judith M. S. Prewitt

! National Institute of Health

i John M. Evans, Jr.

I Office of Developmental Automation and Control Technology, NBS

1 The content of the original image may be significantly altered in reducing it

I to data on a magnetic tape. Optical system response, sensor response, electronic processing and digitization and all will affect the data recorded.

I This session will identify the major problems and discuss the documentation

I needed to describe the relation of the original object to the data recorded

j on tape.

I Panelists:

Wayne Huelskoetter

I Dicomed Corp.

Kendall M. Preston, Jr.

1 Carnegie Mellon University

j James Greenleaf

I Mayo Clinic

Werner Frei

I University of Southern California

J. K. Zieniuk

Bureau of Radiological Health

109
Marvin Maxwell

National Aeronautics and Space Administration

Brent Baxter

University of Utah

Joseph Boccia, M.D.

U. S. Army

10:30 AM COFFEE

10:45 AM RECORDING ENVIRONMENT (CONT.)

12:00 PROTOTYPE IMAGES

Chairpersons:

Theodosius Pavlidis

Princeton University

Roger N. Nagel

National Institute of Health

The selection and distribution of prototype images would assist researchers

and lead to better intercomparison of alternative approaches. The panel will

address the following two issues: (1) the selection of criteria for the evaluation of prototype images and (2) the solicitation of data bases for a centrally

maintained file of prototype images.

Panelists

Jack Sklansky

University of California, Irvine

Sam Dwyer

University of Missouri

Fred Billingsley

Jet Propulsion Lab

12:30 PM LUNCH

1 :30 PM PROTOTYPE IMAGES (CONT.)

3:30 PM ADJOURN

110
LIST OF ATTENDEES

WORKSHOP ON STANDARDS FOR IMAGE PATTERN RECOGNITION

June 3-4, 1976

James S. AT bus

Office of Developmental Automation and

Control Technology

National Bureau of Standards

A130, Technology Building

Washington, D.C. 20234

Bill Alford

NASA-GSFC

Code 931 Greenbelt, MD 20771

Anthony J. Barbera

Office of Developmental Automation and

Control Technology

National Bureau of Standards

A130, Technology Building

Washington, D.C. 20234

Brent Baxter

University of Utah

Salt Lake City, Utah 84112

Harold Berger

B-354, Bldg. 223

National Bureau of Standards

Washington, D.C. 20234

Fred C. Billingsley

NASA

600 Independence Avenue

Washington, D.C. 20546

Joseph Boccia, M.D.

U.S. Army

13908 Dowlais Drive

Rockville, MD 20853

John S. Dehne

U.S. Army Night Vision Laboratory

Attn: DRSEL-NV-VI

Ft. Bel voir, VA 22060

Samuel J. Dwyer III University of Missouri

12 Spring Valley Rd. Columbia, MO 65201

John M. Evans, Jr. Office of Developmental Automation and

Control Technology

National Bureau of Standards

A130, Technology Building

Washington, D.C. 20234

Martin A. Fischler

Lockheed

966 Bonneville Way

Sunnyvale, CA 94087

Herbert Freeman

RPI Troy, NY 12181

Werner Frei use

Dept. EE, PHE 320

Los Angeles, CA 90007

K. S. Fu Purdue University

West Lafayette, IN 47907

James S. Gibbons

U.S. Postal Service

11711 Parkway Drive

Rockville, MD 20852

James F. Greenleaf

Mayo Clinic

Rochester, MN 55901

Kenneth C. Hayes

University of Maryland

Computer Science Center

College Park, MD 20742

Sandra Hawley

ESL, Inc. 495 Java Drive

Sunnyvale, CA 94086

Wayne R. Huelskoetter

Di corned Corp.

9700 Newton Ave. , South

Minneapolis, MN 55431

Mas a Kidode

Purdue University

School of Electrical Engineering

West Lafayette, IN 47901

Russell Kirsch

National Bureau of Standards

A317, Administration Building

Washington, D.C. 20234

111
W. J. McClain

BCTIC, Oak Ridge National Laboratory

P.O. Box X, Bldg. 6025

Oak Ridge, TN 37830

David Mil gram

University of Maryland

Computer Science Center

College Park, MD 20742

Roger N. Nagel

National Institutes of Health

Bethesda, MD 20014

Frank E. Nowak

Educational Projects, Inc. 4616 Henry Street

Pittsburg, PA 15213

Don J. Orser

A317, Administration Building

National Bureau of Standards

Washington, D.C. 20234

Malcolm Ottaway

General Electric

105 E. Monroe Avenue

Alexandria, VA 22301

John O'Callaghan

University of Maryland

Computer Science Center

College Park, MD 20742

Judith Prewitt

National Institute of Health

Rm. 20523, Building 12A

Bethesda, MD 20014

Theo Pavlidis

Dept. EECS

Princeton University

Princeton, NJ 08540

Milton Ritter

NASA

Code 943

Greenbelt, MD 20771

Azriel Rosenfeld

University of Maryland

Computer Science Center

College Park, MD 20742

Jack Skiansky

University of California

School of Engineering

Irvine, CA 92717

John Y. Sos

NASA/GSFC

Code 463

Greenbelt, MD 20771

Robert Sproull

Xerox Corp.

333 Coyote Hill Road

Palo Alto, CA 94304

Gordon VanderBrug

National Bureau of Standards

A130, Technology Building

Washington, D.C. 20234

J. K. Zieniuk

Bureau of Radiological Health

5600 Fimers La. (HFX-210)

Rockville, MD 20852

112

ft U. S. GOVERNMENT PRINTING OFFICE : 1977.
NBS-1 14A (REV. 7-73)

U.S. DEPT. OF COMM. 1 . PU B L IC ATION OR R E PORT NO . 2. Gov't Accession BIBLIOGRAPHIC DATA No SHEET NBS SP-500-8

3. Rec ipicnt '.s Accession No.

4. TITLE AND SUBTITLE (.Q^py^j^ SCIENCE & TECHNOLOGY:

Woricshop on Standards for Image Pattern Recognition

Proceedings of a Workshop Held at the National Bureau of

Standards, Gaithersburg, MD, June 3-4j 1976

5. Publication Date May 1977

6. Performing Organization Code

7. AUTHOR(S)

John M. Evans, Jr., Russell Kirsch, and Roger N. Nagel, F.ditors

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

UtrAKIMtNl Ur CUMMcKCc

WASHINGTON, D.C. 20234

10. Project/Task/Work Unit No.

11. Con t rac t /Gran t No.

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

National Bureau of Standards; Electronic Industries Association,

Washington D. C. 20006; Institute of Electrical & Electronic

Engineers, New York, N.Y. 10017; in cooperation with:

Association for Computing Machinery, New York, N.Y. IOO36

13. Type of Report & Period Covered

14. Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

Library of Congress Catalog Card Number: 77-4773

16. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here.) Automatic image pattern recognition techniques have been successfully applied to

improving productivity and quality in both manufacturing and service applications.

Automatic Image Pattern Recognition Algorithms are often developed and tested using

unique data bases for each specific application. Quantitative comparison of different

approaches and extrapolation of existing techniques to new applications is difficult

or impossible.

To facilitate data interchange in this area a two day workshop was held at the National

Bureau of Standards in Gaithersburg, Maryland on June 3 and 4, 1976.

The workshop considered the issues involved with interchange of images as data in

standard formats on magnetic tape. Specifically, the workshop addressed the following

objectives

1. To define mechanisms for achieving a standard format for magnetic tape interchange

2. To define requirements for documentation of the recording environment of an image.

3. To recommend mechanisms for selecting and distributing prototype images.

4. To consider the requirements and to explore the prospect for a language to descrit

image content and structure.

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

)

Automation; calibration; data formats; documentation; image content language;

image processing; pattern recognition; prototype images; standards.

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