Self-Organization Map Based Segmentation of Breast CancerAuthor : A. Arokiyamary Delphina, M. Kamarasan and S. Sathiamoorthy
Volume 7 No.2 July-December 2018 pp 31-36
Breast cancer is second major leading cause of cancer fatality in women. Mammography prevails best method for initial detection of cancers of breast, capable of identifying small pieces up to two years before they grow large enough to be evident on physical testing. X-ray images of breast must be accurately evaluated to identify beginning signs of cancerous growth. Segmenting, or partitioning, Radio-graphic images into regions of similar texture is usually performed during method of image analysis and interpretation. The comparative lack of structure definition in mammographic images and implied transition from one texture to makes segmentation remarkably hard. The task of analyzing different texture areas can be considered form of exploratory report since priori awareness about number of different regions in image is not known. This paper presents a segmentation method by utilizing SOM.
Breast Cancer, Mammography, Self-Organizing Map, Euclidean Distance, Validity Measure, Double Bouldin Index
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