Self-Organization Map Based Segmentation of Breast CancerAuthor : A. Arokiya Mary Delphia , M. Kamarasan and S. Sathiamoorthy
Volume 7 No.1 January-June 2018 pp 31-36
Breast cancer is the second major leading cause of cancer fatality in women. Mammography prevails the best method for initial detection of cancers of the 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 the breast must be accurately evaluated to identify beginning signs of cancerous growth. Segmenting, or partitioning, radiographic images into regions of similar texture is usually performed during the method of image analysis and interpretation. The comparative lack of structure definition in mammographic images and the implied transition from one texture to makes segmentation remarkably hard. The task of analyzing different texture areas can be considered a form of the exploratory report since a priori awareness about the number of different regions in the image is not known. This paper presents a segmentation method by using SOM.
Breast Cancer, Mammography, Self-Organizing Map, Euclidean Distance, Validity Measure, Double Bouldin Index
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