Ebook: Mammographic Image Analysis

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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The key contribution of the approach to x-ray mammographic image analysis developed in this monograph is a representation of the non-fatty compressed breast tissue that we show can be derived from a single mammogram. The importance of the representation, called h_int, is that it removes all those changes in the image that are due only to the particular imaging conditions (for example, the film speed or exposure time), leaving just the non-fatty `interesting' tissue. Normalising images in this way enables them to be enhanced and matched, and regions in them to be classified more reliably, because unnecessary, distracting variations have been eliminated. Part I of the monograph develops a model-based approach to x-ray mammography, Part II shows how it can be put to work successfully on a range of clinically-important tasks, while Part III develops a model and exploits it for contrast-enhanced MRI mammography. The final chapter points the way forward in a number of promising areas of research.
Audience: This book has been written for a wide readership, including medical image analysts, medical physicists, radiologists, breast surgeons, and research students. The mathematics and algorithms have been relegated to boxes so that the book can be read and understood even if the mathematical detail is skipped. Large parts of the monograph will be of interest to clinicians generally and to patients.

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The key contribution of the approach to x-ray mammographic image analysis developed in this monograph is a representation of the non-fatty compressed breast tissue that we show can be derived from a single mammogram. The importance of the representation, called h_int, is that it removes all those changes in the image that are due only to the particular imaging conditions (for example, the film speed or exposure time), leaving just the non-fatty `interesting' tissue. Normalising images in this way enables them to be enhanced and matched, and regions in them to be classified more reliably, because unnecessary, distracting variations have been eliminated. Part I of the monograph develops a model-based approach to x-ray mammography, Part II shows how it can be put to work successfully on a range of clinically-important tasks, while Part III develops a model and exploits it for contrast-enhanced MRI mammography. The final chapter points the way forward in a number of promising areas of research.
Audience: This book has been written for a wide readership, including medical image analysts, medical physicists, radiologists, breast surgeons, and research students. The mathematics and algorithms have been relegated to boxes so that the book can be read and understood even if the mathematical detail is skipped. Large parts of the monograph will be of interest to clinicians generally and to patients.
Content:
Front Matter....Pages i-xi
Front Matter....Pages 1-1
Introduction....Pages 1-27
Front Matter....Pages 29-29
A Model of Mammogram Image Formation....Pages 31-55
A Model of Scattered Radiation....Pages 57-76
A Model of Extra-Focal Radiation....Pages 77-89
Estimating the Thickness of a Compressed Breast....Pages 91-102
Model Verification and Sensitivity....Pages 103-120
Front Matter....Pages 121-121
Image Enhancement....Pages 123-142
Disease Simulation....Pages 143-150
Breast Compression....Pages 151-174
Removing the Anti-Scatter Grid....Pages 175-190
Calcifications....Pages 191-223
Curvilinear Structures....Pages 225-250
Masses....Pages 251-284
Front Matter....Pages 285-285
Breast MRI....Pages 287-331
Other Modalities and Future Prospects....Pages 333-356
Back Matter....Pages 357-379

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The key contribution of the approach to x-ray mammographic image analysis developed in this monograph is a representation of the non-fatty compressed breast tissue that we show can be derived from a single mammogram. The importance of the representation, called h_int, is that it removes all those changes in the image that are due only to the particular imaging conditions (for example, the film speed or exposure time), leaving just the non-fatty `interesting' tissue. Normalising images in this way enables them to be enhanced and matched, and regions in them to be classified more reliably, because unnecessary, distracting variations have been eliminated. Part I of the monograph develops a model-based approach to x-ray mammography, Part II shows how it can be put to work successfully on a range of clinically-important tasks, while Part III develops a model and exploits it for contrast-enhanced MRI mammography. The final chapter points the way forward in a number of promising areas of research.
Audience: This book has been written for a wide readership, including medical image analysts, medical physicists, radiologists, breast surgeons, and research students. The mathematics and algorithms have been relegated to boxes so that the book can be read and understood even if the mathematical detail is skipped. Large parts of the monograph will be of interest to clinicians generally and to patients.
Content:
Front Matter....Pages i-xi
Front Matter....Pages 1-1
Introduction....Pages 1-27
Front Matter....Pages 29-29
A Model of Mammogram Image Formation....Pages 31-55
A Model of Scattered Radiation....Pages 57-76
A Model of Extra-Focal Radiation....Pages 77-89
Estimating the Thickness of a Compressed Breast....Pages 91-102
Model Verification and Sensitivity....Pages 103-120
Front Matter....Pages 121-121
Image Enhancement....Pages 123-142
Disease Simulation....Pages 143-150
Breast Compression....Pages 151-174
Removing the Anti-Scatter Grid....Pages 175-190
Calcifications....Pages 191-223
Curvilinear Structures....Pages 225-250
Masses....Pages 251-284
Front Matter....Pages 285-285
Breast MRI....Pages 287-331
Other Modalities and Future Prospects....Pages 333-356
Back Matter....Pages 357-379
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