Indian Journal of Science and Technology

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Background/Objectives: In this paper we segment breast and brain Magnetic Resonance Images. Methods/Statistical Analysis: This automated process implemented by a robust Fuzzy C-Means (FCM). This FCM needs novel objective function. This is obtained by performing replacement. The source is original Euclidean distance. Findings: The target is properties of kernel function on feature space. This transformation uses Tsallis entropy. The effective objective functions are minimized. It results in membership partition matrices and successive prototypes with equation. The initial cluster reduces both the running time and computational complexity. The synthetic image with benchmark dataset used to perform initial experimental work. Then it is applied to real breast and brain magnetic resonance image on different region. Conclusion/Improvements: The silhouette method shows better segmentation than existing method.

Keywords

Center Knowledge, Clustering, Fuzzy C-Means, Image Segmentation, Kernal Function, MR Imaging
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