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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 hint, 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.
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.
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.
This book provides a detailed assessment of the state of the art in automated techniques for the analysis of digital mammogram images. Topics covered include a variety of approaches for image processing and pattern recognition aimed at assisting the physician in the task of detecting tumors from evidence in mammogram images. The chapters are written by recognized experts in the field and are revised versions of papers selected from those presented at the “First International Workshop on Mammogram Image Analysis” held in San Jose as part of the 1993 Biomedical Image Processing conference.
This book provides a detailed assessment of the state of the art in automated techniques for the analysis of digital mammogram images. Topics covered include a variety of approaches for image processing and pattern recognition aimed at assisting the physician in the task of detecting tumors from evidence in mammogram images. The chapters are written by recognized experts in the field and are revised versions of papers selected from those presented at the “First International Workshop on Mammogram Image Analysis” held in San Jose as part of the 1993 Biomedical Image Processing conference.
In June 1998 the Fourth International Workshop on Digital Mammography was held in Nijmegen, The Netherlands, where it was hosted by the department of Radiology of the University Hospital Nijmegen. This series of meetings was initiated at the 1993 SPIE Biomedical Image Processing Conference in San Jose, USA, where a number of sessions were entirely devoted to mammographic image analysis. At very successful subsequent workshops held in York, UK (1994) and Chicago, USA (1996), the scope of the conference was broadened, establishing a platform for presentation and discussion of new developments in digital mammog raphy. Topics that are addressed at these meetings are computer-aided diagnosis, image processing, detector development, system design, observer performance and clinical evaluation. The goal is to bring researchers from universities, breast cancer experts, and engineers together, to exchange information and present new scientific developments in this rapidly evolving field. This book contains all the scientific papers and posters presented at the work shop in Nijmegen. Contributions came from as many as 20 different countries and 190 participants attended the meeting. At a technical exhibit companies demon strated new products and work in progress. Abstracts of all papers were reviewed by members of the scientific committee. Many of the accepted papers had excellent quality, but due to limited space not all of them could be included as full papers in these proceedings. Papers that were rated high by the reviewers are included as long or short papers, others appear as extended abstracts in the last chapter.
The identification and interpretation of the signs of breast cancer in mammographic images from screening programs can be very difficult due to the subtle and diversified appearance of breast disease. This book presents new image processing and pattern recognition techniques for computer-aided detection and diagnosis of breast cancer in its various forms. The main goals are: (1) the identification of bilateral asymmetry as an early sign of breast disease which is not detectable by other existing approaches; and (2) the detection and classification of masses and regions of architectural distortion, as benign lesions or malignant tumors, in a unified framework that does not require accurate extraction of the contours of the lesions. The innovative aspects of the work include the design and validation of landmarking algorithms, automatic Tabár masking procedures, and various feature descriptors for quantification of similarity and for contour independent classification of mammographic lesions. Characterization of breast tissue patterns is achieved by means of multidirectional Gabor filters. For the classification tasks, pattern recognition strategies, including Fisher linear discriminant analysis, Bayesian classifiers, support vector machines, and neural networks are applied using automatic selection of features and cross-validation techniques. Computer-aided detection of bilateral asymmetry resulted in accuracy up to 0.94, with sensitivity and specificity of 1 and 0.88, respectively. Computer-aided diagnosis of automatically detected lesions provided sensitivity of detection of malignant tumors in the range of [0.70, 0.81] at a range of falsely detected tumors of [0.82, 3.47] per image. The techniques presented in this work are effective in detecting and characterizing various mammographic signs of breast disease.
Publisher's Note: Products purchased from 3rd Party sellers are not guaranteed by the Publisher for quality, authenticity, or access to any online entitlements included with the product. The 4th Edition of Mammographic Imaging: A Practical Guide remains the most up-to-date and comprehensive book in the field. A perfect all-in-one solution for coursework, board prep, and clinical practice, this bestseller reflects the latest ARRT educational and certification exam requirements, as well as the ASRT recommended curriculum. Technologists seeking to stay current in the profession and students preparing to enter the field will appreciate the 227 new photos, the wide range of case studies, and the interactive online exam simulator with ARRT registry-style questions.
Due to the increasing number of digital mammograms and the advent of new kinds of three-dimensional x-ray and other forms of medical imaging, mammography is undergoing a dramatic change. To meet their responsibilities, medical physicists must constantly renew their knowledge of advances in medical imaging or radiation therapy, and must be prepared
"This publication, the third edition of Mammographic Imaging: A Practical Guide, retains information on analog mammography, builds upon ongoing developments for breast imaging, and introduces new trends in the field of breast imaging. Specifically, thereare five chapters related to digital mammography that address digital technology (machines, image acquisition, image manipulation, and storage), QC, comparisons to imaging with analog mammography, and changes in workflow for the mammography technologist"--Provided by publisher.