Dynamic noise self-recovery ECM clustering algorithm with adaptive spatial constraints for image segmentation

Abstract

Evidence c-means(ECM) has certain advantages in dealing with uncertainty and imprecision, and it is widely applied to data clustering and image segmentation. However, ECM does not utilize spatial information and unable to recover noise, resulting in poor performance for noisy image segmentation. To address these problems, we propose a dynamic noise self-recovery ECM clustering algorithm with adaptive spatial constraints for image segmentation. The proposed algorithm has the following novelties. Firstly, the non-local spatial information is modified by initializing the noise probability to obtain more reliable spatial information. Secondly, the adaptive constraint factors are constructed by using the absolute difference between the original image and the modified non-local spatial information, which can reduce the sensitivity of the algorithm to noise. Finally, the self-recovery factors are constructed on the basis of the neighborhood belief degrees. And a dynamic anti-noise distance is proposed to replace the Euclidean distance. The dynamic anti-noise distance is more suitable for noise self-recover, enabling noise self-recovery during the iterative process. Extensive experiments on synthetic, natural, SAR and MR images show that the proposed algorithm has good performance for image segmentation.