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Books > Science & Mathematics > Physics > Applied physics & special topics > Biophysics
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Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer (Paperback)
Loot Price: R1,392
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Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer (Paperback)
Series: Synthesis Lectures on Biomedical Engineering
Expected to ship within 18 - 22 working days
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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 Tabar 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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