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Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer (Synthesis Lectures on Biomedical Engineering) - Softcover
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.
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About the Author:
Paola Casti graduated with honors in Medical Engineering in 2011 from the University of Rome Tor Vergata, with a thesis titled "Development and validation of a computer-aided detection system for the identification of bilateral asymmetry in mammographic images." In 2015, she received a Ph.D. in Telecommunications and Microelectronics Engineering with the mention of Excellent Quality Cum Laude. The title of her dissertation was "Development of an innovative system for early detection and characterization of breast cancer." During her engineering studies, she was a four-time winner of the annual student award of excellence (given to the top 1% of engineering students) from the University of Rome Tor Vergata. In 2009, she was awarded 1st place in a student competition for the mechanical analysis and computer-aided design of a patented prosthesis, with a project titled "Multi-configurable Wrist Joint Prosthesis." She collaborated with the National Institute of Health (Istituto Superiore di Sanitá, ISS) in 2009 on a project for microtomographic evaluation of bone substitutes and the obtained results have been published in the Rapporti ISTISAN of the ISS. In 2012, she was selected as one of the young researcher participants of the IEEE EMBS International Summer School on Biomedical Imaging, held in Île de Berder, Brittany, France. She has coauthored several papers in international peer-reviewed journals, a number of papers in proceedings of international conferences, and technical reports. Her research interests are in signal and image analysis for medical applications, pattern recognition and classification, and computer-aided diagnosis. At present, she is working with the Department of Electronics Engineering of the University of Rome Tor Vergata with a research contract.
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- PublisherMorgan & Claypool Publishers
- Publication date2017
- ISBN 10 1681731568
- ISBN 13 9781681731568
- BindingPaperback
- Number of pages188
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Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer (Synthesis Lectures on Biomedical Engineering)
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Morgan & Claypool Publishers
(2017)
ISBN 10: 1681731568
ISBN 13: 9781681731568
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Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer (Synthesis Lectures on Biomedical Engineering)
Published by
Morgan & Claypool Publishers
(2017)
ISBN 10: 1681731568
ISBN 13: 9781681731568
New
Soft cover
Quantity: 1
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Book Description Soft cover. Condition: New. 8vo (23.5 cm), XX, 166 pp. Laminated wrappers. Synopsis: 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. Seller Inventory # 008479
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