1. Purdue November 1998 Slide 1 Computer Aided Diagnosis in Digital Mammography Sheng Liu Charles F. Babbs Edward J. Delp Purdue University West Lafayette, Indiana, USA http://www.ece.purdue.edu/~ace

2. Purdue November 1998 Slide 2 Sheng Liu

3. Purdue November 1998 Slide 3 Outline Overview the Breast Cancer Problem Mammographic Features of Breast Abnormalities Normal Mammogram Analysis and Recognition Further Research

4. Purdue November 1998 Slide 4 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

5. Purdue November 1998 Slide 5 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

6. Purdue November 1998 Slide 6 Screening Mammography

7. Purdue November 1998 Slide 7 A Digital Mammogram (normal)

8. Purdue November 1998 Slide 8 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

9. Purdue November 1998 Slide 9 Three Types of Breast Abnormalities Micro- calcifications Circumscribed Lesion Spiculated Lesion

10. Purdue November 1998 Slide 10 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

11. Purdue November 1998 Slide 11 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

12. Purdue November 1998 Slide 12 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

13. Purdue November 1998 Slide 13 Malignant Masses High density radiopaqueSolid tumor, may be smooth or lobulated, random orientation

14. Purdue November 1998 Slide 14 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

15. Purdue November 1998 Slide 15 Benign Masses Radiolucent densityRadiolucent and radiopaque combined Low density radiopaque

16. Purdue November 1998 Slide 16 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

17. Purdue November 1998 Slide 17 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

18. Purdue November 1998 Slide 18 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

19. Purdue November 1998 Slide 19 Normal Recognition Strategy

20. Purdue November 1998 Slide 20 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

21. Purdue November 1998 Slide 21 Very Different Normal Mammograms Density 1Density 2Density 3Density 4

22. Purdue November 1998 Slide 22 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

23. Purdue November 1998 Slide 23 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

24. Purdue November 1998 Slide 24 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

25. Purdue November 1998 Slide 25 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

26. Purdue November 1998 Slide 26 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

27. Purdue November 1998 Slide 27 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

28. Purdue November 1998 Slide 28 Test Pattern and Angle Image Test PatternAngle 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

29. Purdue November 1998 Slide 29 Line Detectors’ Output 0o0o 14 o 26 o 37 o 45 o 53 o 64 o 76 o

30. Purdue November 1998 Slide 30 Line Detectors’ Output (Cont.) 90 o 104 o 116 o 127 o 135 o 143 o 154 o 166 o

31. Purdue November 1998 Slide 31 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

32. Purdue November 1998 Slide 32 Test Mammogram A circumscribed lesion appears against normal background

33. Purdue November 1998 Slide 33 Background Subtraction

34. Purdue November 1998 Slide 34 Normal Structure Detection

35. Purdue November 1998 Slide 35 Sample Line Detectors’ Output 0o0o 45 o 135 o 90 o

36. Purdue November 1998 Slide 36 Normal Line Mask I m Angle imageNormal line mask I m is obtained from the angle image by morphological opening to get rid of isolated responses then morphological closing to connect broken lines

37. Purdue November 1998 Slide 37 Normal Structure Removal

38. Purdue November 1998 Slide 38 Further Research

39. Purdue November 1998 Slide 39 Conclusion Future work includes further testing the normal detection system Mammographic image databases and database management