In this paper

an improved fuzzy clustering based segmentation algorithm for noise images is presented. Being sensitive to noise is one of the popular fuzzy C means (FCM) algorithm’〖KG-*3〗s drawbacks. The new objective function used in fuzzy clustering is modified to obtain different membership functions by rewarding crisp membership degrees

that is

using distances to the Voronoi cell instead of using distances to the cluster prototypes. Thus the proposed algorithm enhances robustness to noise. Experimental segmentation results for noise images demonstrate the effectiveness and robustness capability of the proposed algorithm.