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Algorithms for detection and classification of abnormality in mammograms: An overview
Published in IGI Global
Pages: 255 - 280
Mammography is a popular imaging modality currently in use for routine screening of breast. Radiologists look for some of the significant signs of breast cancer while examining the mammogram visually. These signs are bounded masses, clusters of micro-calcifications, spiculations, and architectural distortions. Developing computer-aided algorithms for the detection and classification of abnormalities in mammograms is an extremely challenging task because of significant variableness in the type, size, shape, texture variation of abnormal region, and variability in the structure of surrounding tissues of the breast. The main objective of this chapter is to introduce dominant features of various signs of abnormalities and to discuss techniques to detect various abnormalities in mammograms. This knowledge will help to develop a system that is useful for the early detection and classification of breast cancer. © 2018, IGI Global. All rights reserved.
About the journal
JournalHandbook of Research on Information Security in Biomedical Signal Processing
PublisherIGI Global