Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy Image Segmentation

Abstract

Although the evidence c-means clustering (ECM) has the capability to process uncertain information, it is not suitable for noisy image segmentation, because the spatial information of pixels is not considered. To solve the problem, an adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation algorithm is proposed. Firstly, an adaptive noise distance that can be iteratively updated is constructed using the local information of the pixels. Secondly, to improve the classification performance, an adaptive kernel function is proposed to measure the distance between the pixel and the cluster center. Simultaneously, the original, local and non-local information of pixels are introduced adaptively into the objective function to enhance the robustness to noise. In the iteration, the noise cluster is automatically recovered using the recovery factor constructed by the gray and spatial information of neighborhood pixels. Finally, the credal partition is transformed into a fuzzy partition by pignistic transformation, the classification of pixel be determined by the maximum membership principle. Experiments on synthetic images and real images demonstrate that the proposed algorithm has strong noise suppression ability. Visual effects and evaluation indexes verify the effectiveness of the proposed algorithm for noisy image segmentation.