Robust Fuzzy-c-means for Image Segmentation

Abstract

Fuzzy-c-means (FCM) algorithm is widely used for magnetic resonance (MR) image segmentation. However, conventional FCM is sensitive to noise because it does not consider the spatial information in the image. To overcome the above problem, an FCM algorithm with spatial information is presented in this paper. The algorithm is realized by integrating spatial contextual information into the membership function to make the method less sensitive to noise. The new spatial information term is defined as the summation of the membership function in the neighborhood of pixel under consideration weighted by a parameter α to control the neighborhood effect. This new method is applied to both synthetic images and MR data. Experimental results show that the presented method is more robust to noise than the conventional FCM and yields homogenous labeling.