A robust fuzzy clustering algorithm using mean-field-approximation based hidden Markov random field model for image segmentation

Although how to deal well with images corrupted with noise is a commonly encountered task in image segmentation, the design of efficient and robust segmentation algorithms still keeps a challenging research topic. In this paper, a robust fuzzy clustering-based image segmentation algorithm is presented to effectively segment noisy images. The proposed algorithm is derived from both the conventional fuzzy c-means (FCM) clustering algorithm and the hidden Markov random field (HMRF) model with the capability of incorporating spatial information. The performance of the proposed algorithm is experimentally evaluated with the comparison algorithms. Experimental results on synthetic and real images demonstrate the effectiveness of the proposed algorithm.