Robust credibilistic intuitionistic fuzzy clustering for image segmentation

To improve the anti-noise ability of credibilistic intuitionistic fuzzy c-means clustering method (CIFCM) for image segmentation, this paper proposes a robust credibilistic intuitionistic fuzzy c-means clustering method based on credibility of pixels and intuitionistic fuzzy entropy. Firstly, a new similarity measure is constructed by utilizing the grayscale and spatial relationship between the current pixel and its neighborhood pixels. Secondly, it is embedded into the objective function of credibilistic intuitionistic fuzzy c-means clustering, and a new robust clustering method with spatial constraints is presented to effectively solve the segmentation problem of image corrupted by high noise. In the end, the convergence of this proposed robust clustering method is strictly proved by iterated convergence theorem. Experimental results show that proposed algorithm has better noise-suppression ability and more satisfactory segmentation results than CIFCM algorithm.