An effective fuzzy clustering algorithm for image segmentation

Fuzzy c-means (FCM) with spatial constraints has been considered as an effective algorithm for image segmentation. In this paper, we propose a new algorithm to incorporate the local spatial information with the consideration of mean template. Our algorithm is fully free of the empirically predefined parameters that are used in other FCM methods to balance between robustness to noise and effectiveness of preserving the image sharpness and details. Furthermore, in our algorithm, the prior probability of an image pixel is influenced by the fuzzy memberships of pixels in its immediate neighborhood to incorporate the local spatial information and intensity information. Finally, we utilize the mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. Compared to HMRF, our method is simple, easy and fast to implement.

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