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Copyright © 2015
M. S. Thirumalai
Publisher: M.S. Thirumalai
11249 Oregon Circle
Bloomington, MN 55438
Mammographic Microcalcification Segmentation Using
Fuzzy C Means Clustering
Nishanthi.C., M.E. Student
Breast cancer is one of the major causes of death among women. Mammography is the main test used for screening and early diagnosis. Early detection performed on X-ray mammography is the key to improve breast cancer prognosis. This paper presents a research on mammography images using Morphological operators and Fuzzy c – means clustering for cancer tumor mass segmentation. The first step of the cancer signs detection should be a segmentation procedure able to distinguish masses and micro calcifications from background tissue using Morphological operators and finally fuzzy c- means clustering (FCM) algorithm has been implemented for intensity – based segmentation. This method does not require any manual processing technique for classification, thus it can be assimilated for identifying benign and malignant areas in intelligent way. Moreover it gives good classification responses for compressed mammogram image. The goal of the proposed method is twofold: one is to preserve the details in Region of Interest (ROI) at low bit rate without affecting the diagnostic related information and second is to classify and segment the micro-calcification area in reconstructed mammogram image with high accuracy. The experimental result shows that the proposed model performance is good at achieving high sensitivity of 97.27%, specificity of 94.38%.
Keywords: Mammography, Micro calcification, Segmentation, Fuzzy c- means clustering.
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Mekala. S., M.E.
Nishanthi. C., M.E. Student
Department of Electronics & Communication Engineering
Sri Subramanya College of Engineering & Technology
NH - 209, Sukkamanaickenpatti