A Robust Segmentation Approach for Noisy Medical Images Using Fuzzy Clustering With Spatial Probability

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Executive Summary

Image segmentation plays a major role in medical imaging applications. During last decades, developing robust and efficient algorithms for medical image segmentation has been a demanding area of growing research interest. The renowned unsupervised clustering method, Fuzzy C-Means (FCM) algorithm is extensively used in medical image segmentation. Despite its pervasive use, conventional FCM is highly sensitive to noise because it segments images on the basis of intensity values. In this paper, for the segmentation of noisy medical images, an effective approach is presented. The proposed approach utilizes histogram based Fuzzy C-Means clustering algorithm for the segmentation of medical images.

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