Digital Case Library: AResource for Teaching,Learning, and DiagnosisSupport in Radiology1Katarzynaj Macura, MD, PhD #{149}Robert T Macura, MD, PhD

Brandon D. Morstad, MS

The authors compiled a digital case library, a database of cases for intracranial

masses, that can be used as an electronic teaching file and image source for

case-based teaching applications, electronic textbooks, and diagnosis support

tools. The library is a relational database with a feature-coded image archive

that is structured around relevant radiobogic fmdings. Its index for coding im-age content is structured as a hierarchical image description index that uses therelational format. Rules that control the search direction within the library andthat generate lists of diagnostic hypotheses for decision support tools are em-bedded within the database structure. Currently, the library consists of 200cases and 1,100 images that present intracranial masses on radiographs, corn-puted tomograms, magnetic resonance images, and angiograms. Each image inthe library is indexed according to its radiologic content. The user may searchfor reference images that contain particular radiologic features by formulatingimage content-based queries. The hierarchical index of radiologic fmdings at-lows multilevel query formulation that depends on the user’s level of expeni-

ence.

. INTRODUCTIONFor radiologists, the image is the basic carrier of information. When making a diagnosis,

radiologists frequently access image collections for reference. The most commonlyused sources of radiologic reference images are atlases and teaching files, whether film-,

slide-, or laser disk-based. Computer-based applications such as electronic textbooks,

knowledge-based systems, and databases constitute other sources of images (1-5).

Abbreviations: ACR = American College of Radiology, CD-ROM = compact disk, read-only memory, 4D = 4th Dimen.

sion

Index terms: Computers, diagnostic aid #{149}Education #{149}Images, interpretation

RadloGraphlcs 1995; 15:155-164

I From the Department of Radiology, Medical Physics Section, Medical College of Georgia, 1459 Laney Walker Blvd. AE-

2018, Augusta, GA 30912-3950. Presented as an infoRAD exhibit at the 1993 RSNA scientific assembly. Receivedjuly 15.

1994; revision requested August 25 and received September 19; accepted September 27. Address reprint requests to

K.J.M.,-RSNA. 1995

155

156 #{149}infoRAD Volume 15 Number 1

We are building a digital case library-a radi-

obogy feature-coded image database-that can

be searched for a combination of radiologic

findings. An image database may be queried for

many purposes: (a) to retrieve an image of a

specific finding, a set of fmdings, or a diagnosis;

(b) to identify the finding or diagnosis for an im-age; (c) to retrieve an image similar to a target

image; and (cO to retrieve images that share

findings but have entirely different diagnoses

(differential diagnosis). These needs will be met

if the image content within the database can be

encoded by using either automatic image-under-

standing (ie, image recognition and classffica-

tion) methods or verbal descriptors for the do-

main-specific logic that underlies the image

findings.

In our project, we focus on the design of a

structure for classifying verbal descriptors for

radiobogic images that represent image content

at different levels of specifIcity. The challenge

is to determine a way to transform radiologic

knowledge, beyond the American College of Ra-

diobogy (ACR) code, into usable indexes. An-

other major technical challenge is the design of

the retrieval process-from formulation of a

query, through the search, to the retrieval of

the relevant images. The success of a particular

retrieval process will depend on the way in

which images are indexed and in which the rel-

evance of images is assigned.

In this article, we describe the structure of a

feature-coded image database that uses a hierar-

chical index of radiobogic findings to encode

the image content.

S DESCRIPTION OF A DIGITAL CASE

UBRARY

. Overview

Our digital case library is implemented as a rela-tional database. The basic data structure sup-

ported by a relational database is the table,

which consists of columns that correspond to

fields identifying a specific property being

stored and rows that identify records. The cells

contain values. The tables represent relations.

The advantage of storing data in the rela-

tional format is that the independence of the

data is preserved, which means that the data

are not associated with any particular applica-

tion. The digital case library is intended to pro-

vide (a) reusable data for many different appli-

cations and (b) multilevel access to data (ie, the

data can be accessed on many different levels,

depending on the user’s experience).

The potential multilevel query formulation

needs to be translated into a form that is recog-

nizable by the database. This translation re-

quires an organization of database indexes that

will be capable of conveying relationships be-

tween, and classification of, the image descrip-

tors. The formation of a hierarchy is a useful

way of achieving the desired organization. The

essence of the hierarchy is a representation of a

tree that has a root node, and every node on

subsequent levels has only one parent. Al-

though hierarchical databases support the ar-

rangement of data into tree structures, the hier-

archical model requires that the database be de-

veloped for a specific application and that the

data are accessed in a preestablished way. The

relational database provides the flexibility nec-

essary to create a hierarchy and, unlike the hi-

erarchical database, guarantees data indepen-

dence.

Our digital case library is implemented in

4th Dimension (4D) (ACI US, Cupertino, Calif),

a relational database management system, on

the Macintosh (Apple Computer, Cupertino,

Calit) platform. 4D Universal, which is a plat-

form-independent version of 4th Dimension, is

available for Power Macintosh, Windows (Mi-

crosoft, Redmond, Wash), and Sun (Sun Micro-

systems, Sunnyvale, Calif) systems. 4th Dimen-

sion can be used as a client/server front end

tool, not only for 4D Server (ACI US) but also

when other structured query language (SQL)databases are used.

The cases included in the digital case library

were selected from neuroradiobogic images in

the Medical College of Georgia archives. The

present library consists of 200 cases and 1,100

images, including radiographs, computed to-

mographic (CT) scans, magnetic resonance

(MR) images, and angiograms, demonstrating

intracranial masses.

. File OrganizationThe structure of the database is determined by

the way we define a radiologic case. A case inour database is referred to as an example that

presents a particular abnormality pertaining to

a particular body location. The case is defmed

on the basis of the diagnosis, anatomic location

of the lesion, and age group of the patient.

Thus, the same patient in different life periods

BasicObservations

Lesion characteristics

CT auenuation MR signal intensity

/“�,/ TI weighted

Hyperattenuating Isoattenuating Hypoattenuating

/\Interpretations � Blood Calcified Solid mass

January 1995 Macura et al N RadioGraphics #{149}157

Figure i#{149}Diagram depicts relation-ships between files within the digital

case library structure. DDX = differ-ential diagnosis.

Figure 2. Diagram illustrates a partof the hierarchical structure of radio-

logic fmdings that are classified asbasic observations and interpreta-

tions.

with different abnormalities in different ana-

tomic locations may appear in as many cases as

there are different age groups, anatomic boca-tions, and diagnoses.

Each case has a set of images, which are

grouped according to their modality. Each im-

age is indexed on the basis of its radiologic

content. Radiobogic fmdings (indexes) are di-vided into three groups: lesion location, lesion

characteristics, and associated fmdings. There

are 1 1 main files within the digital case library

(Fig 1): (a) files related to the Image file (ie,

those that store information about the images

that belong to the case)-the Lesion Location

file, Lesion Characteristics file, and Associated

Findings file; (b) files related to the Case file-

the Image file, Differential Diagnosis file, and

Report file; (c) files related to the Person file

(ie, those that store information defming a case

and the patient medical history)-the Case file,

Previous Pathology file, and Clinical History

file; and (�:I) a Person file, which stores per-

sonal data that is related to the Hospital file.

. Image Indexing with Hierarchies

To encode the image content in terms of radio-

logic findings, we have designed a hierarchical

image description index that allows image in-

dexing in neuroradiobogy (6). We use a seman-

tic hierarchy, composed of basic observations

and interpretations, that forms a continuum, in

which higher level findings incorporate lower

level findings.

In this approach, the image details are

coded by using both basic observations (such

as the attenuation of cerebral contents relative

to normal brain tissue observed on the CT

scans) and findings that express interpretation

of basic observations (eg, calcification, blood,

cyst, fat) (Fig 2).

158 #{149}info RAD Volume 15 Number 1

A hierarchy tree is implemented as a series

of database records, with the record keys repre-

senting nodes in the tree (Fig 3). A record key

is composed of slots that correspond to the 1ev-

els in the hierarchy. A slot typically contains an

alphanumeric code of one character (or more),

which enables subsequent searching to be

made with an alphanumeric key. The first slot

of a record key corresponds to the highest level

in a hierarchy (root level of a tree), and the last

slot used corresponds to the lowest level in the

hierarchy (leaf node of a tree). A record key

contains as many slots as there are levels in the

hierarchy. A particular record key inherits all of

the slots of its immediate parent and uses the

additional slot to uniquely identify itself. The

hierarchy can easily be updated and expanded

interactively with the user’s assistance.

Each image in the digital case library has a

caption attached to it. The hierarchical image

description index allows decomposition of the

caption into searchable key words for encoding

the image content. The image caption “coronal

Ti-weighted image obtained after injection of

gadolinium shows a ring-enhancing mass

within the left parietal lobe with associated

moderate vasogenic edema” will be indexed as

shown in the Table (Fig 4). Moreover, each im-

age in the library has an assigned age group

that is based on the patient’s date of birth and

the examination date for a particular study. All

index terms are searchable database fields that

may be used for image retrieval.

. Image Acquisition

Film images in the existing digital case library

were digitized with a laser scanner (Lumiscan

1 00; Lumisys, Sunnyvale, Calf) at high resolu-

tion (1 25 �im spot size) with 4,096 (1 2 bits)

gray levels. A single retrieved image can be dis-

played at 5 1 2 x 5 1 2 resolution with 256 (8 bits)

shades of gray. Some images were transferred

directly from the MR imaging unit (Signa; GE

Medical Systems, Milwaukee, Wis) to the digital

Hierarchical structure of record keysRecords

IAI I II’�I’�I IIAIAIAIIAIAIBIIAIBfllAid I

IBI I IIBIAI IFigure 3- Diagram shows a hierarchy tree in the

format of relational database records. The first slot

of a record key corresponds to the highest level in a

hierarchy, and the last slot corresponds to the low-est level.

Indexing of a Sample Ima ge Caption

Index Term Value

Procedure MR imagingView CoronalPhase Ti weighted

Contrast PostcontrastLesion location Parietal lobe

Lesion characteristics

Pattern of enhancement RinglikeAssociated findings Edema, moderate

vasogenicity

case library on the Macintosh computer by us-

ing Fetch (public domain software; Dartmouth

College, Hanover, NH). NIH Image (public do-

main software; National Institutes of Health,

Bethesda, Md) was used to import generic file

formats (given the header and image matrixspecifIcations) and to convert the 16-bit 256 x

256 MR image data to an 8-bit gray-scale tagged

image file format (TIFF) file. We used AdobePhotoshop (Adobe Systems, Cupertino, Calif)

to enhance, crop, and rescale images.

January 1995 Macura et al U RadioGraphics #{149}159

- � - . ���tEctitIE�ayt- ---. -

ii�eq. sember � _�_�_- hesqs srder 1i�TLas_s date l4L±�:�’.��j � � �

rP��5d0�’_ _----__---�_._ _____________________________________________-

� 0 eqiogr.p�v Pt.. lloronei � �.. 0 CT C�trsst �‘ost-contrest

� #{149}Mrn #{248}essel I� O�ucserm.iiceate pt,ese lii weigtita*t

� opI.ians.

� OUitrsss.i.d

rile’,. CMFCtaV*$UCSLeslee lecatiso Skull erw5 contents flretn end mnlngu. supretenteflaLParletal lobe 4i�i�iL� �

� Lesisit chsrsctar$stlcs rIRI Contrast enh�scement:Pettsm of .nhencemsntRlng-flke

� �t�d____________$ss.chsted fladtas IlRt!iiernePrscent Vesogentc Moderate

� �.�-im.qecapt�a-�--------�---- ..- - ------------____________________________________� �:ot-oise1 T 1 weghted image after Gadolinium shows a ring enhancIng �� mass within the left perletol lobe with associated modrete vesogntc I

edeme.

Figure 4. Image input screen

presents fields for coding infon-

mation that describes the im-

age: procedure; lesion location,which follows ACR code; Ic-

sion characteristics; associated

findings, which follow the hien-

anchical image description in-

dcx; and image caption. Theage group is computed basedon the birth date of the patient

and the examination date.

Images are stored in PICT (picture) format

external to the database on an image server that

is accessible through the local area network

(LAN) and on a compact disk, read-only mem-

ory (CD-ROM) for stand-alone applications. The

image server is accessible through the 10

Mbytes/sec Ethernet. The transfer rate for the

dual-speed CD-ROM drive is 300 Kbytes/sec.

Image files are compressed by using a Joint Pho-

tographic Experts Group UPEG) compression

algorithm supplied with QuickTime (Apple

Computer). The compression ratio for high-

quality images is 3.5: 1 . After compression, a

single image occupies about 55 Kbytes.

Manual coding of radiobogic features for a

single image takes about 1 minute. Because the

knowledge-based indexing is used for each case

in the digital case library, an additional 30 see-

onds per image are spent by a radiologist to

verify the presence of features important for re-

tnieving each image. The average time needed

for a single image to be incorporated into the

digital case library (from image acquisition,

through processing and indexing, to verifica-

tion) is 20 minutes.

. Query Formulation and Image Re-

trieval

Information retrieval is a process in which the

information needs of users are compared with

the information that is available. Information

need is represented as a query, and potential in-

formation (images) is represented as a collec-

tion of index terms that can be matched against

the query. The images with index terms thatmost closely match the query are then assumed

to be relevant and are passed to the user.

To initialize the search for reference images,

the user may enter as many search criteria as

desired at the desired level of specificity. A

checklist-type entry menu that lists the findings

useful in diagnosis of intracranial masses is dis-

played. The user inputs a value for each radio-

logic finding listed in the entry menu, employs

a pop-up menu for entering the structured in-

formation according to the hierarchical image

description index, and specifies lesion location

according to the ACR code. For example, if a ra-

ls&ul 1 u� c�st.#{149}Its�#{149}‘ a m� mmhiss. s&#{231}rMmtonsl

�!r1.t.t toe.

Et5I$IS ilestiss � -�� �“Irt#{149}ts1toOl I

�-SIs �-

i ap lea1-cas. ei.e.e�I

[Infection

pr.e.._. e�srsttsdsties

k.icltt.d

r5smdstsd �

I Ilv�mIc.�

Figure 5. Search criteria en-

try screen.

.� ...- �. I

E2� �

160 U infoRAD Volume 15 Number 1

0a.g�.��#{149}CTO’sON.tIw.sIkI.s0 Plea mm0 UItrsssssd

0 ISI�t

0 casiOams�st.sto,s�g �t

#{149}5l111�

a- I

c�*.si ��‘.-c..tr�st

Vess.I Ipe.� I

diobogist observes a hyperattenuating mass on

an unenhanced CT scan, is not sure whether

the lesion represents blood or a calcified

mass, and would like to retrieve images from

the digital case library that show a similar pat-

tern, he or she may search for hyperattenu-

ating lesions and the set of retrieved cases will

contain both hematomas and calcified masses.

If the user wants to search for different pat-

terns representing calcified lesions, the re-

turned set of images will present only hyper-

attenuating calcified lesions (Fig 5).

The search criteria are transformed into the

search keys in a way similar to the manner in

which the record keys are built, with the dif-

ference being that they can be considered par-

tial keys (ie, they usually contain only a part of

the record key). A search in the database with

a partial search key would return a selection

of records that would belong to a hierarchi-

cally organized sub-tree (Fig 6).The present retrieval method in the digital

case library is based on template matching.

Database records are retrieved only if they

match precisely the constraints expressed in

the query; that is, if the sum of features in the

input case matches those of cases in the li-

brary. On the basis of patient age and lesion 1-

cation (mandatory inputs) as well as the clini-

cal history and past medical history (optional),

the differential diagnosis list is formulated. This

list is retrieved from the decision table imple-

mented in the database structure. The decision

table uses two diagnostic cues (patient age and

location of the lesion) and the judgment of

neuroradiobogy experts regarding the probabil-

ity of a given diagnosis (7). The cases that rep-

resent pathologic conditions listed in the differ-

ential diagnosis list and that match the user’s

search criteria are returned to the user.

. Digital Case Library FunctionsThe digital case library is intended to be used

as an electronic teaching file of intracranial

masses and a case resource for case-based

teaching applications, electronic textbooks,

and diagnosis support tools.

We are using the digital case library in a

multiuser configuration that is accessible

through the departmental network (4D Server

and 4D Client; AC! US). Each user may access

I. Search key

IAI? I I

Selected records

tAT I IIAIAI IIAIAIAI

IAIAIBIIAIBI I1A1I IIA � (� IAF�1 1][�{AII

2. 1A I A � ? 1AA B C ________Search key

Selected nodes in the hierarchy

January 1995 Macura et al #{149} RadioGrapbics U 161

Selected nodes in the hierarchy Selected records

_______ I”i I I______ AIAI

___ �1�J�11�1:J�______ BIAI I

Figure 6. Diagrams depict the selection of records matching two search keys. All records (ie, cases) that be-long to a hierarchically organized sub-tree are returned to the user. Selected records are indicated by the an-rows and boxes.

Figure 7. Quiz screen shows

a randomly chosen case withthe caption, diagnosis, and dif-fenential diagnosis windows,which have been displayed asrequested by the user.

the library through a database located on a

server connected to his or her own Macintosh

computer.

The library contains a quiz module that may

be used for self-testing. Case images are ran-domly displayed for the user, who may then

formulate a diagnosis for an unknown case.

Each image is displayed with three blank win-dows that will provide information about the

case when requested by the user: a caption

window, a diagnosis window, and a differential

diagnosis window (Fig 7). The user may

choose the amount of information needed tomake a diagnosis or may view the diagnosis as

desired.

GOoblestome multi? oi-rne

----

r � infsra?tan -

(esierstrelsed: 14

,�s

pseetch criterI.�------- �------------�- -

sq. Adult

M.d�tV �

. Lecstloa Skull and contents Brain end meninges, gupretentorlalPrsetel iota

Curr.nthj rsferssc#{149}d teas - -- -

Ce.. h*s1or� �

�k Prec#{149}diires Itsi :

I Oueiuiew] r �

162 � infoRAD Volume 15 Number 1

Figure 8. Display case screen.On the basis of the search crite-ria defined by the user, relevant

cases are retrieved along withthe list of differential diagnoses.The user may browse throughthe cases, zoom images, access

image captions, and change the

procedure.

Case-based teaching relies on the principle

that learners will most naturally structure theirown knowledge bases and gain correlative cx-

perience by visually memorizing radiobogic pat-

terns and deliberating over cases. Our case Ii-

brary is accessible to any applications that use

cases for instruction (8).

As a diagnosis support tool, the digital case

library helps the user reach a diagnosis by pro-

viding images from proved cases that match the

description of a specific case being evaluated

and by providing a differential diagnosis list. On

the basis of the search criteria defined by the

user, relevant images and a textual description

are produced for comparison with the case in

question. The user may browse through re-

tnieved cases, zoom images, or make a correla-

tion between different procedures (Fig 8).

S Hardware RequirementsA Macintosh II computer with 16 Mbytes of ran-

dom access memory (RAM) and a CD-ROM

drive are the minimum equipment required for

running the system as a stand-alone application.

A 16-inch monitor is also needed. Any of the

Macintosh II series computers with an 832 x

624 x 24-bit color display graphics card may be

used.

U DISCUSSIONThere are several difficulties with encoding ra-

diobogic findings in a database of a digital caselibrary. The ACR code represents a substantial

amount of information about anatomy and diag-

nostic categories, which permits the radiologist

to code almost any radiobogic diagnosis. It also

allows the addition of a procedural code, but it

does not provide an effective way to capture

image content in terms of radiologic findings.

An index that describes the image-related data

(ie, verbal expressions for radiobogic image con-

tent) is needed so that anatomy, pathology, and

procedure fields from the ACR code can be cx-

tended to a fourth dimension: In other words,

such an index is needed so that radiobogic find-

ings and images can be tagged for later retriev-

ab. The designers of Image/Icon have developed

a prototype of image description language for

pathologic conditions of the chest that allows

dynamic selection of images along axes of clini-

cal relevance (9). In our work, we attempted to

design a fourth field for the ACR code that

would allow feature coding in neuroradiology.

We structured radiologic findings as a hierarchi-

cal image description index for coding lesion

characteristics and associated lesion findings.

The form of indexing required in a database

depends on the types of queries anticipated.The terms used in the index must be selected

on the basis of the vocabulary that a user might

January 1995 Macura et al U RadioGrapbics U 163

be expected to employ, and the index must in-

corporate the concepts normally used to de-

scribe the data being indexed. During report

generation, radiologists use verbal expressions

to describe the image content; we hope to cap-

tune those expressions as components of the hi-

erarchical image description index to allow ra-

diologists to retrieve the same images from the

database later when needed. Depending on the

expertise of the radiologist, image findings are

communicated at different levels, ranging from

the beginner’s purely perceptual level to the

expert’s highly interpretative bevel. The poten-

tial multilevel query formulation needs to be

translated into a form that is recognizable by

the database. This translation requires an orga-

nization of the vocabulary used to classify terms

and convey relationships between them. Thus,

designing an image database that is accessible

to users with different levels of experience re-

quires multilevel indexing of image content

(10). The hierarchical image description index

offers a useful way of achieving the desired

multilevel organization. This type of concept in-

dexing requires a representation of a knowl-

edge domain that is typically seen in an expert

system. The framework of an intelligent data-

base is formed when the classification scheme

required for an expert system can be integrated

with a database system.

Currently, our digital case library does not a!-

low automatic encoding of image content. We

are implementing a parsing module that will au-

tomatically decompose image captions consist-

ing of natural language expressions into search-

able key words. Computerized extraction of di-

agnostic information from free-text radiology

reports has already been explored by other re-

searchers (1 1).

Building a feature index for coding imaging

findings serves two goals: (a) providing a con-

sistent method of coding (both manual and au-

tomatic) and (b) guiding image retrieval. As us-

ers attempt to find information in an inconsis-

tently indexed database, that inconsistency will

be propagated into uncertainty as to how a par-

ticular information need can be expressed as a

query. The development of a well-defined

query language has the potential of improving

information accessibility because the searcher

uses a highly constrained vocabulary correctly.

Improved performance is also obtained by sim-plifying the task of data entry. Our design pro-

vides the user with the possibility of searching

for images by using verbal expressions en-

coded into a hierarchical index of radiologic

findings. The use of a hierarchical image de-

scription index for coding image content per-

mits the expression of image content-based

queries. Because each radiologic feature is

identified by name in the index used by the da-

tabase as well as by its associated image, it is

possible to retrieve all images that contain a

given feature.

The present retrieval method in the digital

case library is through template matching. The

limitation of this deterministic retrieval method

is that images are retrieved only if they matchprecisely the constraints expressed in the

query based on a sum of features in the input

case that matches cases in the library. This

method will preclude any match other than an

exact match and does not reflect the perfor-

mance of the human perceptual system, which

retains the identity of images under certain

transformation.

Other retrieval methods, such as a nearest

neighbor method, allow the user to locate

cases that are somewhat similar to the new

case. This approach is most effective if the

number of cases in the library is relatively

small, if the cases are incompletely described,

or if new cases are likely to match the existing

cases only vaguely. However, the use of a near-

est neighbor retrieval approach leads to retriev-

al times that increase linearly with the number

of cases in the library because each case in the

library needs to be considered. Bramble et al

(1 2) have described the results of a nearest

neighbor retrieval method applied to radiologic

teaching files. In our approach, the digital case

library is intended to contain enough cases to

define clearly most of the diagnostic catego-

ries (ie, the library contains as many cases as

164 U info RAD Volume 15 Number 1

needed to illustrate each of the many different

patterns possible for a particular abnormality,

not just one example per diagnosis). We have

estimated that we would need about 100 more

cases (about 500 images) to cover sufficiently

the domain of intracranial masses.

We are currently testing the digital case li-

brary with end users (ie, radiology residents and

faculty) at the Medical College of Georgia, and

we are preparing a version for testing outside

the development laboratory. Eventually, we in-

tend to distribute the digital case library on CD-

ROM and envision its use in the picture archiv-

ing and communication system (PACS) and

teleradiobogy environment. The preliminary re-

sults have shown that our digital case library is

successful in providing efficient access to refer-

ence cases, but further testing in clinical set-

tings will show whether the system design has

potential for becoming a welcome addition to

daily diagnostic practice in radiology.

Acknowledgments: The authors thank Eugene F.

Binet, MD, chairman of the department of radiology,

and Jon H. Trueblood, PhD, director of multimedia

radiology educational products, at the Medical Col-

lege of Georgia for their contributions to and sup-

port of the project. Many thanks go to Cliff W. Garz-

zillo, Jr, digital photographer; Robert V. Finkbeiner,

MS, medical illustrator; and Ted B. Kingsbury IV, MA,

software and hardware engineer. We also thank the

reviewers for their comments on the manuscript.

S REFERENCES1 . Jaffe CC, Lynch PJ, Smeulders AWM. Hyper-

media techniques for diagnostic imaging in-

struction: videodisk echocardiography encyclo-

pedia. Radiology 1989; 171:475-480.

2. D’Alessandro MP, GalvinJR, Erkonen WE, et

al. The instructional effectiveness of a radiol-

ogy multimedia textbook (HypenLung) versus astandard lecture. Invest Radiol 1993; 28:643-

648.

3. Swett HA, Fisher PR, Cohn A!, Miller PL, Mu-

talik PG. Expert system-controlled image dis-

play. Radiology 1989; 172:487-493.

4. Swett HA, Mutalik PG. Multipurpose image

and data management system for radiologists.

In: Brody WR, Johnston GS, eds. Computer ap-

plications to assist radiology. Carlsbad, Calif:

Symposia Foundation, 1992; 101-107.

5. Taira RK, Candenas AF, Chu WW, Bneant CM,

Hall T. A knowledge-based multi-media data-

base system for skeletal radiology. In: Lemke

HU, Inamura K, Jaffe CC, Felix R, eds. Com-

puten assisted radiology. Berlin, Germany:

Springer-Verlag, 1993; 649-654.

6. Macura RT, Macura KJ, Monstad BD, Binet EF,

Truebbood JH. Feature-coded image database.

In: Boehme JM, Rowberg AH, Wolfman NT,

eds. Computer applications to assist radiology.

Carlsbad, Calif: Symposia Foundation, 1994;

313-318.

7. Macura RT, Macuna KJ. The usefulness of de-

cision tables in encoding of medical knowl-edge. In: Proceedings of the Third Symposium

of the International Association of Knowledge

Engineers lAKE ‘92. Gaithensburg, Md: Interna-

tional Association of Knowledge Engineers,

1992; 263-269.

8. Macuna RT, Macura KJ, Tono YE, Binet EF,

Truebbood JH, Ji K. Computerized case-basedinstructional system for computed tomog-raphy and magnetic resonance imaging of

brain tumors. Invest Radiol 1994; 29:497-506.

9. Cohn A!, Miller PL, Fisher PR, Mutalik PG,Swett HA. Knowledge-based radiologic im-

age retrieval using axes of clinical relevance.

Comput Biomed Res 1990; 23:199-221.

10. Gneenes RA, McClure RC, Pattison-Gondon E,

Sato L. The findings-diagnosis continuum: im-

plications for image descriptions and clinical

databases. In: Proceedings of the 16th Sympo-

sium on Computer Applications in MedicalCare. New York, NY: McGraw-Hill, 1992; 383-387.

1 1 . Haug PJ, Ranum DL, Frederic PR. Computer

extraction of coded findings from free-text ra-

diologic reports. Radiology 1990; 174:543-

548.

12. BrambleJM, Insana MF, Dwyer SJ III. Infor-

mation retrieval for teaching files: a pnelimi-

nary study. J Digital Imaging 1990; 3:164-169.