Purdue November 1998 Slide 1 Computer Aided Diagnosis in Digital Mammography Sheng Liu Charles F. Babbs Edward J. Delp Purdue University West Lafayette,

Purdue November 1998 Slide 1

Computer Aided Diagnosis inDigital Mammography

Sheng LiuCharles F. BabbsEdward J. Delp

Purdue University West Lafayette, Indiana, USA

http://www.ece.purdue.edu/~ace


Sheng Liu


Outline

• Overview the Breast Cancer Problem

• Mammographic Features of Breast Abnormalities

• Normal Mammogram Analysis and Recognition

• Further Research


Breast Cancer

• Breast Cancer is the second leading cause of death in women in the United States (after lung cancer)

• 1 in 8 women will develop breast cancer

• Evidence seems to indicate that “curable” tumors must be less than 1 cm in diameter

• Screening mammography is currently the best technique for reliable detection of early non-palpable cancer


Mammography

• In the United States, it is recommended that women over 50 years old receive annual mammograms

– higher risk subpopulation over 40 years old

• Usually 4 views are taken (2 of each breast)

–most mammograms are taken using X-Ray film (analog)

– digital mammogram systems are now being deployed


Screening Mammography


A Digital Mammogram (normal)


Digital Mammography

• Resolution - 50 pixel size

– 3000 x 4000 pixels (12,000,000 pixels)

– 8-16 bits/pixels• 8 bits/pixel (12 MB)

• 16 bits/pixel (24 MB)

• Each study consists of 48-96 MB!

• 200 patients per day can results to 20GB/day

• Problems with storage and retrieval


Three Types of Breast Abnormalities

Micro-calcifications

Circumscribed Lesion

Spiculated Lesion


Malignant Microcalcifications

Extremely variable in form, size, density, and number, usually clustered within one area of the breast

Granular:

dot-like or elongated, tiny, innumerable

Casting:

fragments with irregular contour, differ in length


Benign Microcalcifications

Homogenous, solid, sharply outlined,

spherical, pearl-like, very fine and dense

Crescent-shaped or elongate

Ring surrounds dilated duct, oval or elongated, varying lucent center, very dense periphery

Linear, often needle like, high and uniform density


Benign Microcalcifications

Ring-shaped, oval, center radiolucent, occur within skin

Egg-shell, center radiolucent or of parenchymal density

Coarse, irregular, sharply outlined and

very dense

Similar to raspberry, high density but often contain

small, oval-shaped lucent areas


Malignant Masses

High density radiopaque Solid tumor, may be smooth or lobulated, random orientation


Benign Masses

Halo: a narrow radiolucent ring or a segment of a ring around

the periphery of a lesion

Capsule: a thin, curved, radiopaque line that surrounds lesions containing

fat

Cyst: spherical or ovoid with smooth borders, orient in the direction of the

nipple following the trabecular structure of the breast


Benign Masses

Radiolucent density Radiolucent and radiopaque combined

Low density radiopaque


Malignant Spiculated Lesions

Scirrhous carcinoma:

distinct central tumor mass, dense spicules radiate in all directions, spicule length

increases with tumor size

Early stage scirrhous carcinoma:

tumor center small, may be imperceptible, only a lace-like, fine reticular radiating

structure which causes parenchymal distortion and/or asymmetry


Benign Spiculated Lesions

Sclerosing ductal hyperplasia:

translucent, oval or circular center, the longest spicules are very thin and long, spicules close to the lesion center become numerous and clumped

together in thick aggregates

Traumatic fat necrosis:

translucent areas are within a loose, reticular structure, spicules are fine and of low density


Identification of Normal Mammograms

• >95% of all mammograms are normal

• Little work has been done on recognizing normal mammograms

• Propose to prescreening mammograms to identify the relatively large number of clearly normal mammograms, as well as large areas of clearly normal tissue in potentially abnormal mammograms

• Substantially reduce the work load of radiologists and increase the accuracy of their diagnosis on subtle cases

Sheng Liu, Charles F. Babbs, and Edward J. Delp


Normal Recognition Strategy


Advantages of Normal Recognition

• Fundamentally simpler — characteristics of normal tissue are relatively simpler than characteristics of tumors of various types, sizes, and stages of development

• Easier to test and validate the performance — the number of normal mammograms is much larger than the number of mammograms with any specific abnormalities

• Facilitates the classification of abnormalities — suppressing normal structures essentially enhances signal-to-noise ratio of abnormal structures


Very Different Normal Mammograms

Density 1 Density 2 Density 3 Density 4


General Normal Characteristics

• Unequivocally normal areas have lower overall density than abnormal ones

– no spikes indicating microcalcifications

– no large bright areas indicating masses

• Normal areas have “quasi-parallel” linear markings


Normal Linear Markings

• Shadow of normal ducts and connective tissue elements

• Appear slightly curved

• Approximately linear over short segments

• Can be observed as straight line segments of dimensions 1 to 2 mm or greater in length and 0.1 to 1.0 mm in width

• Low contrast in very noisy background


Problems in Detecting Linear Markings

• Edge extraction based line detectors

– generate very dense edge maps due to small spatial extent of most local edge operators

– do not distinguish between lines and object boundaries

• Hough transform based line detectors

– do not provide locations of lines

– not suitable for grayscale images


Normal Line Detectors

Specially designed a set of correlation filters to detect normal linear markings at 16 radial orientations

filter impulse response of line detectors


Edge Suppression Factor

• We want to detect lines, not edges

– similar grayscale values at both sides of a line

– significant difference in grayscale values at different sides of an edge or object boundary

• An “edge suppression factor” is used to suppress response to edges


Detect Normal Linear Markings

• By adjusting “backbone” and “base” widths, line detectors can be tuned to respond to lines of any desired thickness

• Normal linear markings in mammograms are about 0.1 to 0.5 mm thick


Test Pattern and Angle Image

Test Pattern Angle Image

An angle image is obtained by taking maximum of the 16 line detectors’ output at each pixel location and then assigning its pixel value in proportion to the corresponding orientation


Line Detectors’ Output

0o 14o 26o 37o

45o 53o 64o 76o


Line Detectors’ Output (Cont.)

90o 104o 116o 127o

135o 143o 154o 166o


Database

• Digital Database for Screening Mammography (DDSM) provided by Massachusetts General Hospital, University of South Florida, and Sandia National Laboratories

• 42 / 50 • More than 650 cases available now

• Each case consists of 4 images: left and right MLO and CC views

• Have pixel level “ground truth” information


Test Mammogram

A circumscribed lesion appears against normal background


Background Subtraction


Normal Structure Detection


Sample Line Detectors’ Output

0o 45o

135o90o


Normal Line Mask Im

Angle image Normal line mask

Im is obtained from the angle image by

• morphological opening to get rid of isolated responses

• then morphological closing to connect broken lines


Normal Structure Removal


Further Research


Conclusion

• Future work includes further testing the normal detection system

• Mammographic image databases and database management

Documents

Purdue November 1998 Slide 1 Computer Aided Diagnosis in Digital Mammography Sheng Liu Charles F. Babbs Edward J. Delp Purdue University West Lafayette,