Computer Aided Diagnosis in Digital Mammography

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

2. Sheng Liu

3. Outline • Overview the Breast Cancer Problem • Mammographic Features of Breast Abnormalities • Normal Mammogram Analysis and Recognition • Further Research

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. 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. Screening Mammography

7. A Digital Mammogram (normal)

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. Three Types of Breast Abnormalities Micro-calcifications Circumscribed Lesion Spiculated Lesion

10. Extremely variable in form, size, density, and number, usually clustered within one area of the breast Malignant Microcalcifications Casting: fragments with irregular contour, differ in length Granular: dot-like or elongated, tiny, innumerable

11. Benign Microcalcifications Homogenous, solid, sharply outlined, spherical, pearl-like, very fine and dense Crescent-shaped or elongate Linear, often needle like, high and uniform density Ring surrounds dilated duct, oval or elongated, varying lucent center, very dense periphery

12. Benign Microcalcifications Egg-shell, center radiolucent or of parenchymal density Ring-shaped, oval, center radiolucent, occur within skin Similar to raspberry, high density but often contain small, oval-shaped lucent areas Coarse, irregular, sharply outlined and very dense

13. Malignant Masses High density radiopaque Solid tumor, may be smooth or lobulated, random orientation

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. Benign Masses Radiolucent density Radiolucent and radiopaque combined Low density radiopaque

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. 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. Identification of Normal Mammograms Sheng Liu, Charles F. Babbs, and Edward J. Delp • >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

19. Normal Recognition Strategy

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. Density 1 Density 2 Density 3 Density 4 Very Different Normal Mammograms

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. 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. 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. 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. 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. 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. Test Pattern and 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 Test Pattern Angle Image

29. Line Detectors’ Output 0o 14o 26o 37o 45o 53o 64o 76o

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

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. Test Mammogram A circumscribed lesion appears against normal background

33. Background Subtraction

34. Normal Structure Detection

35. Sample Line Detectors’ Output 0o 45o 90o 135o

36. Normal Line Mask Im • Im is obtained from the angle image by • morphological opening to get rid of isolated responses • then morphological closing to connect broken lines Angle image Normal line mask

37. Normal Structure Removal

38. Further Research

39. Conclusion • Future work includes further testing the normal detection system • Mammographic image databases and database management