Total Bregman divergence-based fuzzy local information C-means clustering for robust image segmentation

Abstract

The fuzzy local information C-means clustering algorithm (FLICM) is an important robust fuzzy clustering segmentation method, which has attracted considerable attention over the years. However, it lacks certain robustness to high noise or severe outliers. To improve the accuracy and robustness of the FLICM algorithm for images corrupted by high noise, a novel fuzzy local information c-means clustering utilizing total Bregman divergence (TFLICM) is proposed in this paper. The total Bregman divergence is modified by the local neighborhood information of sample to further enhance the ability to suppress noise, and then modified total Bregman divergence is introduced into the FLICM to construct a new objective function of robust fuzzy clustering, and the iterative clustering algorithm with high robustness is obtained through optimization theory. The convergence of the TFLICM algorithm is proved by the Zangwill theorem. In addition, the validity of the TFLICM algorithm applied in noise image segmentation is explained by means of sample weighting fuzzy clustering. Meanwhile, the generalized total Bregman divergence unifies the Bregman divergence with the total Bregman divergence and enhances the universality of the TFLICM algorithm applied in segmenting complex medical and remote sensing images. Some experimental results show that the TFLICM algorithm can obtain better segmentation quality and stronger anti-noise robustness than the existing FLICM algorithm.