Pulmonary Nodules: Computer-aided Detection in ...

Pulmonary Nodules: Computer-aided Detection in Digital Chest Images1 Maryellen L. Giger, PhD Kunio Doi,, PhD HeberMacMabon, MD Charles E. Metz, PhD Fang-Fang Yin, MS

Currently,

radiologists

of cases

with

actually

to camouflaging ing

positive

effects

decision

oped

fail to detect

criteria,

chest and

method

images. The feature-extraction

tests.

Computer

to possible

nodule

locations

that

computer

Diagnoses

situations.

missed

due

and

vary-

We

of lung

used

clinical

improve

be

devel-

nodules

on a difference-image including growth,

were

in 60 can

may

locations

is based techniques,

in up to 30%

subjective

in clinical

results

aid

nodules

background,

to detect

method

and profile suggest

findings.

of anatomic or distractions

a computerized

digital proach

pulmonary

to alert

cases. the

1 2 radiologists

Preliminary

detection

in apslope, results

performance

of

radiologists. U INTRODUCTION Currently, radiologists fail to detect pulmonary nodules in up to 30% of cases with actually positive diagnoses (1 ,2) . These diagnoses may be missed because of the camouflaging effect of the surrounding anatomic background, the subjective and varying decision criteria used by radiologists, on various distractions present in the clinical situation (3-7) . If a computerized method could be developed to detect nodules in a digital chest image, then it is expected that radiologists, alerted to possible nodule locations by the computer, would make fewer false-negative diagnoses.

In this paper, we describe our computerized method (8- 1 1) designed to assist radiologists in the detection of lung nodules in digital chest images. The method is based on a difference-image approach and various feature-extraction techniques. Our scheme is different from the computerized detection attempts by other

Abbreviation: Index

ROC

terms:

receiver

Computers,

operating

diagnostic

aid

characteristic. #{149} Lung

neoplasms,

60.3

#{149} Lung

neoplasms,

diagnosis

#{149} Radiography,

comrn

puter.assisted RadloGraphics I

From

the

1990; Kurt

ofChicago, accepted Public ©RSNA,

Rossman

5841 and Health

10:41-51 Laboratories

S Maryland

for

Aye,

revision

requested

Service

grant

Radiologic

Chicago, May

CA24806.

1 1 ; revision Address

Image

IL 60637.

From

Research,

Department

the

RSNA

receivedjune reprint

requests

1988

1 3. Supported

of Radiology,

annual

meeting.

by a Whitaker

Box Received Research

429, March grant

University 3, 1989; and

U.S.

to M.L.G.

1990

41

SPATIAL

FREOUENCY

(CYCLES/MM)

a.

Figure 1. tion scheme.

Schematic

investigators

of the

computerized

(1 2- 1 5) in that

on an approach camouflaging

that

initially

anatomic

ours

reduces

background

detec-

is based

the in a

chest image before the application of featune-extraction techniques. In this article, we also present results of an observer study in which the unaided and computer-aided detection performances of radiologists U

were

compared.

MATERIALS

AND

METHODS

#{149} Equipment

b. Figure 2. (a) Graph illustrates the effects of the signal-enhancing filter, the signal-suppressing fluter, and the difference-image technique in the spatial frequency domain . The signal-enhancing filter

A drum scanner (1 6) is used for

(Fuji Photo Film, Tokyo) the digitization of the clini-

cal radiographs. This film digitizer employs a 0. 1 -mm pixel size and a 1 0-bit analog-to-digital converter. With this system, the digital pixel value (gray level) is approximately un-

is a spatial

sional

profile

en size

and

filter

matched

contrast.

(b)

.

1 1/750 is performed

computer

U

Giger

et a!

two-dimen-

nodule

spatial

density analyzed

(Digital

Marlboro, to reduce

to 5 1 2 X 5 1 2 , yielding of0.6 mm.

RadioGraphics

In the

related to the optical The image data are

poration,

U

typical

of giv-

domain,

the signal-suppressing filter is a ring that replaces the gray level of each pixel by the average of the pixel values that lie along a ring of a given radius (R) and width (W).

early (1 7)

42

to the

of a simulated

of the film on a VAX

Equipment

Mass) the

.

Spatial image

an effective

Volume

10

Con-

averaging matrix

pixel

Number

size

size

1

a.

b.

d.

C.

Figure

3.

digitized difference

An original

chest

radiograph

to produce a signal-enhanced image (512 X 512 matrix,

#{149} Computerized In our computerized

Detection detection

(a)

shows

and

the signal the

January

other

Method scheme,

1990

of the nodule

in which

the

signal

left

lung

image (b), a signal-suppressed 0.6 mm pixels) (d).

the

complex anatomic background is reduced by means of the difference-image approach. From a single projection image of the chest, two filtered images are produced: one in

which

a 1 -cm

nodule

image

(arrow).

(c),

This

radiograph

was

and the corresponding

pressed (Figs 1 -3) . Because the resulting backgrounds in each of the two processed images are similar, the difference between the two processed images consists of an im-

age with

the signal

of the nodule

supenim-

is enhanced, is sup-

Giger

et a!

U

Ra4ioGrapbics

U

43

C’, -J Lu

x

13. U. 0

0.6 Lu I

:

z LU >

-C LU

a:

200

400

PIXEL

600

VALUE

200

800

(GREY

LEVEL)

400

PIXEL

600

VALUE

800

(GREY

LEVEL)

b. Histograms of the original image (a) and the difference image (b) in Figure 3 . In the original image, the pixel value of the nodule is similar to that of other anatomic structures, since the nodule can be superimposed on any structure in the chest. However, in the difference image the pixel value of the nodule is at the high end of the histogram.

a.

Figure

4.

posed which

on a relatively the complexity

is substantially

reduced

(Figs

Pattern-recognition employed the nodule

ISLAND

simple background in of the norma! anatomy 3d,

CIRCLE WITH EQUIVALENT ISLAND AREA

4).

techniques

\

are then

so that the computer from this difference

can detect image. Be-

cause the resulting pixel value of the nodule in the difference image is high and isolated from that of surrounding structures, gray 1evel thresholding of the difference image can be used

ules

as an initial

from

The

other

islands

thresholding

step

in separating

anatomic that images

structures

result

in the

are

measured

gray

AREA OF ISLAND WITHIN CIRCLE

nod5).

(Fig level for

EFFECTIVE

degree

DIAMETER

2JAREA

OF

ISLAND

of circularity and effective diameter. These measures are used to describe the shape and size,

respectively,

of a given

island

(Fig

6).

DEGREE

OF

CIRCULARITY

Figure 6. Diagram shape are determined. as the

same pressed

effective

area

AREA

shows

diameter

as the island.

by the

degree

fined as the ratio the equivalent-area

OFISLAND

how

WIrHIN

island

size

Size of an island of a circle

Shape

and

is defined

that

has

of an island

of circularity,

CIRCLE

which

the

is cxis de-

of the area of the island within circle to the total area of the

island.

44

U

Ra4ioGraphics

U

Giger

et a!

Volume

10

Number

1

p

...,;.

b.

a.

,

..

.

L

t

..,.. .

.

.. ,

C.

d.

.

Figure

5.

The difference

remaining

patterns,

area

the histogram

under

January

1990

which

image, have

subjected pixel

.

values

of the difference

to gray-level

thresholding

in the

5% (a),

image,

upper

are referred

at four different 10%

(b),

20%

(c),

cutoff and

40%

values.

The

(d)

of the

U

RadioGrapbics

to as “islands.”

Giger

et a!

U

45

20

>.

16

Ia:

-C

LU

C-,

LU

a:

C-C

12

D

U.

LU

0

>

Lu LU

8

a:

C-)

0

LU

(0 LU

LU U. U.

5

THRESHOLD

a. Figure 7. for islands

Size nodules

due

(%

LEVEL

OF

Graphs illustrate dependencies of island arising from nodules and nonnodules.

and shape of the and nonnodules

to normal

anatomic

islands arising from (ie, pseudonodules

background)

vary

threshold level (Figs 5 , 7). A growth test is used initially to distinguish nodules from nonnodules. The test is based on the fact that islands arising from nodules maintam a high degree of circularity oven a larger

of threshold

dules.

With

circularity

levels

the

growth

of an

island

than test, falls

do nonnoif the

below

degree

of

a prede-

termined cutoff level within a preset number of consecutive threshold levels, then the island is eliminated from the list of possible nodule sites. A slope test is then used to eliminate some of the false-positive nodules remaining after the growth test. Slope is defined as the ratio of the change in effective island diameter to the change in threshold level. Islands from nonnodules tend to “grow” more rapidly than islands arising from nodules. Thus, if the calculated slope is greater than a predetenmined cutoff value, then the island is re-

moved

from

the

list of possible

nodule

b. circularity

similar cular. lower

with

range

10

THRESHOLD

HISTOGRAM)

sites.

The appearance of a nodule relative to the camouflaging anatomic background will vary depending on the location of the nodule in the chest image. For example, nodules in the peripheral lung region tend to have contrast

(a)

and

island

15

20

LEVEL

size

(% OF

(b)

25

30

35

HISTOGRAM)

on threshold

level

to that of nib edges but are more cirIn the penihilar region, nodules are of contrast than vascular structures, a!-

though

both may appear circular. A profile that employs region-growing techniques is used to distinguish between nodules and nonnodules in the penihilar region of the chest image (Fig 8) . The area of the “grown region” is related to the profile of the suspected feature. We have found that the profiles of nonnodules tend to have smaller areas and higher contrast than those of real nodules. If the area of the grown region is less than a preselected cutoff value, then the location is eliminated as a possible nodule test

site.

#{149} Performance

Evaluation

The computerized detection scheme was used in the evaluation of posteroantenior chest radiognaphs from 60 clinical cases. Thirty cases were normal, and 30 had nodules of varying subtlety and size (5-30 mm). The presence and location of the nodules were verified by means of computed tomography or follow-up radiography. True-positive findings (detected nodules) and falsepositive findings were tabulated automatically by the computer for each image.

#{149} Observer

Study

Twelve radiologists reviewed the same 60 clinical cases that had been used in the evaluation of the computerized detection method. The radiologists interpreted the same ra-

46

U

RadioGraphics

U

Giger

et a!

Volume

10

Number

1

Figure 8 Computer gions for a nodule (a)

image of penihilar and a nonnodule

chest areas generated with region-growing techniques. (b) are shown for equal increments of gray levels.

LI) LU

with

-J

Detection Computer

0 0

z

Accuracy of Results (Level 1)

0.8

z

0 0.6 C-)

LI U-

0.4

> FU,

0 13.

0.2

LU LI

F-

0.c 0

2

FALSE

4

POSITIVES

6

PER

8

CHEST

IMAGE

9. Performance curve of the computerized scheme alone for 60 clinical cases. The curve was generated by varying the range required in the growth test, thus yielding different numbers of true-positive and false-positive detections. The computer program achieves a true-positive rate of approximately 70% with an average of seven to eight false-positive detections per chest image (indicated by arrow). Figure

computer-reported results. levels of computer output were used to investigate the effect of the computer false-positive rate on radiologists’ performance. Level 1 of the computer output corresponds to a 70% true-positive detection accuracy, with an average of about eight falsepositive findings per chest image. Level 2 refers to a simulated computer output with the same 70% true-positive detection accunacy, but with an average of one false-positive finding per chest image. Each of the 1 2 observers participated in three reading sessions, with each session consisting of 60 images with varying conditions (ie, 20 without computer aid, 20 with level 1 output, and 20 with level 2 output); each of the 60 cases was read once during each session. The reading order of the cases was randomized within each condition, and the order of the conditions was systematically varied to reduce the effects of learning and

00

January

1990

the

fatigue U

diognaphs without computer aid and with it using a five-point confidence rating scale to allow for receiver operating characteristic (ROC) analysis (1 8) . A short (5-second) viewing time was used to force errors and simulate the clinical environment. For the computer-aided interpretations, the radiologists viewed the conventional radiograph and an adjacent duplicate image marked

re-

Two

U. 0

LU

Grown

(19).

RESULTS

The computer program achieved a true-positive rate of approximately 70% with an average of seven to eight false-positive detections per chest image (Fig 9) Radiologists reading .

Giger

et a!

U

RadioGraphics

U

47

a. b. Figure 10. Chest radiographs without (a) and with (b) computer-reported theses) in a 44-year-old woman with metastatic breast carcinoma. A single lobe is correctly identified by the computer.

a.

results metastasis

(indicated by paren(T) in the left upper

b.

Figure 11. Chest radiographs without (a) and with (b) computer-reported results (indicated by parentheses) in a 7 1 -year-old asthmatic woman. An 8-mm nodule (1’) in the left upper lobe is correctly identifled by the computer, although it also detected four false-positive lesions.

the same cases, without aid and with 5-second viewing time, achieved a true-positive rate of approximately 75% in visually localizing the nodules. In Figures 1 0- 1 3 , four selected pairs of chest images are shown. The images on the left (Figs lOa, 1 la, 12a, 13a) are the conventional radiographs interpreted by the radiologists; the images on the night (Figs 1 Ob, 1 ib, 1 2b, 1 3b) show the computer-reported

48

U

RadioGrapbics

U

Giger

et a!

results (at level 1) . Results from the computen are indicated by parentheses, and the actual positions of nodules are indicated by the letter T. The combination of a T and parentheses represents a true-positive detection by the computer, whereas parentheses alone and a T alone correspond to false-positive and false-negative computer results, nespectively (none of the latter occurred in this senies of four cases).

Volume

10

Number

1

a. b. Figure 12. Chest radiographs without (a) and with (b) computer-reported results (indicated by parentheses) in a 78-year-old woman with an irregularly marginated nodule in the right upper lobe. This lesion (1’), subsequently proved to be squamous cell carcinoma, is correctly identified by the computer. Two false-positive detections occurred in the left upper lobe.

a. b. Figure 13. Chest radiographs without (a) and with (b) computer-reported theses) in a 67-year-old man with a small cavitary nodule in the left upper the nodule (1’), with four other false-positive lesions. The nodule proved

results (indicated lobe. The computer to be squamous cell

In a case of metastatic breast carcinoma (Fig 1 0) , the nodule was correctly identified

vessels on superimposition lar structures. The nodule

by the computer, with no false-positive detections. All 1 2 radiologists were able to detect the nodule without and with computer

changed

aid. As shown tified a more false-positive tive detections

January

in Figure 1 1 , the computer idensubtle nodule, as well as four lesions. Most of the false-posiwere caused by end-on blood

1990

on multiple

by parenidentified carcinoma.

of ribs remained

subsequent

and

vascuun-

radiographs

and probably represented a granuloma. Only 1 0 radiologists located the nodule correctly without computer aid, whereas all of the nadiologists detected it with the aid.

In a case 1 2), the regularly

of squamous

computer marginated

cell

correctly nodule

Giger

carcinoma detected in the

et a!

(Fig an inright up-

U

RadioGrapbics

U

49

per

lobe.

also

false-positive Neither the

lesions were extremely irreguof the tumor nor its partial superon nibs and clavicle precluded its Two

identified.

lan shape imposition

identification the nodule

by the computer. In this case, was detected by 1 1 radiologists

without use 1 2 radiologists

of the computer with the aid.

aid

and

‘-I ‘-I

(I, D

but also indicated four false-positive lesions. One false-positive lesion was immediately below the center of the true-positive nodule; the computer most likely detected an infeniof the

malignant

nodule.

findings

were

The

caused

oth-

by non-

mal anatomic structures, which usually be easily eliminated from consideration

the

can by

radiologist. In Figure

ologists

14

, the performances

without

with

their

computer

As indicated

of either

level

increased formance.

with

level

by the ROC

of computer

significant

(P

U CONCLUSIONS We have developed for the detection

digital chest tion scheme

1ev-

output

use

to locations

contain

a pulmonary

‘‘ .2).

a computerized scheme of pulmonary nodules in

in a radiograph nodule.

the

complex

chest

extraction techniques of computer-reported

decreased false-positive

tection

image.

of our preliminary a computer aid can

performance

ROC

study

in which

was

used

in the detection

decisions

proved Feature-

the number detec-

ROC study sugimprove the de-

of radiologists.

ilar

may

background

in a single-projection

tions. Results gest that

that

Final

are made by the radiologist. The difference-image technique in reducing

:D I-

0.2

FALSE

U

Ra4ioGrapbics

U

Giger

et a!

0....

POSITIVE

a computer

In a simprogram

of microcalcifica-

tions in mammograms, ly significant increase

.0

FRACTION

(20)

there in the

(The

.

was a statisticalradiologists’

computer

had a

true-positive rate for cluster localization of approximately 90%, compared with the radiologists, who had a true-positive rate for localization of approximately 80% without the aid.) We believe that the improvement in the detectability of pulmonary nodules will be-

come greater as our computerized scheme developed further to increase its sensitivity and

specificity

for nodule

In a clinical tection yoked chest

setting,

procedure

performed

is

detection.

the computerized

de-

scheme could be applied as a test by the radiologist upon viewing a radiograph or as a routine screening

on all chest

in-

examina-

tions. It should be noted that when the detection scheme is applied to less subtle nodules, the true-positive rate increases. In addition, the use of stricter criteria in the

feature-extraction reduction Thus, the configured

techniques

would

in the false-positive computerized system to

allow

the

yield

a

detections. could be

radiologist

to con-

trol the sensitivity and specificity of the computer output. Obviously, a choice of fewer false-positive lesions would be achieved at

the cost

50

0.4

Figure 14. ROC curves show the performance of 1 2 radiologists with and without the computer aid in the interpretation of the same 60 cases used for Figure 9. Solid line performance without aid, line of large dashes performance with level 1 aid, line of small dashes = performance with level 2 aid.

performance

slightly

images. The aim of our detecis to direct the radiologist’s at-

tention

useful

1 and

curves,

the radiologists’ detection penHowever, the increase was not sta-

tistically

aLi

of the nadiare compared

aid

performances

el 2 aid.

cr Li >

Only four radiologists detected the nodule on either conventional radiographs or on images with level 1 computer output, and only seven radiologists found it with level 2 aid. The computer accurately located the nodule

en false-positive

IC3 L

by all

In another case of squamous cell cancinoma (Fig 1 3), the lesion was more subtle.

on portion

z

of a lower

number

Volume

of true-positive

10

Number

1

lesions,

and

vice

be adjusted

versa.

This

tradeoff

by a radiologist,

could

depending

the nature of the case material and personal preference. For example, a radiologist might choose an output with high sensitivity for examining high-risk patients being screened for lung cancer on metastases, whereas a lower sensitivity and correspondingly lower falsepositive rate might be desired for patients at

low

9.

on

1 0.

1 1.

risk for cancer.

ACknowledgments: Carlin, Kyle Van Dyke, Wei

Xu for

their

We are grateful Hai-Bien Wang,

technical

assistance;

to Michael and Xinto Carl J. Vy-

part of the clinical to Heang-Ping Chan, PhD, for use of her computer program to randomize the viewing order of images. We thank Mark H. Diamond, MD, RobertJ. Foust, MD, John D. Hegarty, MD, Charles E. Kahn, MD, Steven M. Montner, MD, Brian C. Randall, MD, Jordan Rosenblum, MD, Yasuo Sasaki, MD, PhD, Robert A. Schmidt, MD, Charlene A. Sennett, MD, and Ella H. Wong, MD, for their participation in the observer study. We also thank Evelyn Ruzich for her bonny,

sions in anatomic background. 1988; 9 14:635-637. 12

.

MD, PhD, for accumulating data base of images; and

secretarial

Giger ML, Doi K, MacMahon H. Computerized detection of lung nodules in digital chest radiographs. Proc SPIE 1987; 767: 384-386. Giger ML, Doi K, MacMahon H. Computeraided diagnosis of pulmonary nodules. In: Peppler \W, Alter AA, eds. Proceedings of the Chest Imaging Conference 1987. Madison, Wis: Medical Physics, 1988; 135-137. Giger ML, Doi K, MacMahon H, Yin F-F. Image-processing techniques used in the computer-aided detection of radiographic IcSPIE

Toniwaki J, Suegana Y, Negoro T, Fukumura T. Pattern recognition of chest x-ray images. Comput Graphics Image Processing 1973;

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Ballard DH, SklanskyJ. A ladder-structured decision tree for recognizing tumors in chest radiographs. IEEE Trans Computers 1976; C-25:503-513. Hashimoto M, Sankar

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PV, SklanskyJ.

ing the edges of lung tumors by iion techniques. In: Proceedings Computer Society, International on Pattern Recognition. IEEE no.

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