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Breast cancer is a major health problem in the Western world, where it is the most common cancer among women. Approximately 1 in 12 women will develop breast cancer during the course of their lives. Over the past twenty years there have been a series of major advances in the manage ment of women with breast cancer, ranging from novel chemotherapy and radiotherapy treatments to conservative surgery. The next twenty years are likely to see computerized image analysis playing an increasingly important role in patient management. As applications of image analysis go, medical applications are tough in general, and breast cancer image analysis is one of the toughest. There are many reasons for this: highly variable and irregular shapes of the objects of interest, changing imaging conditions, and the densely textured nature of the images. Add to this the increasing need for quantitative informa tion, precision, and reliability (very few false positives), and the image pro cessing challenge becomes quite daunting, in fact it pushes image analysis techniques right to their limits.

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Preface. 1. Introduction. Part I: Generating hint. 2. A Model of Mammogram Image Formation. 3. A Model of Scattered Radiation. 4. A Model of Extra-Focal Radiation. 5. Estimating the Thickness of a Compressed Breast. 6. Model Verification and Sensitivity. Part II: Exploiting the hint Model. 7. Image Enhancement. 8. Disease Simulation. 9. Breast Compression. 10. Removing the Anti-scatter Grid. 11. Calcifications. 12. Curvilinear Structures. 13. Masses. Part III: Further Breast Image Analysis. 14. Breast MRI. 15. Other Modalities and Future Prospects.A: Receiver operating characteristic (ROC) curves. Bibliography. Index.

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