An extended fuzzy local information C-means clustering algorithm

Fuzzy c-means clustering algorithm (FCM) is often used for image segmentation but it is sensitive to noise. This paper presents an extended fuzzy local information c-means clustering algorithm for robust image segmentation. In this method, a novel fuzzy factor created by the neighborhood spatial and gray information is integrated into the objective function of FCM. The fuzzy factor can enhance the algorithm´s clustering performance by adjusting the influence of neighboring pixels to the center pixel. The proposed method can not only preserve the image details but also enhance the robustness to noise. Experiments implemented on synthetic images and real images demonstrate that the proposed method achieves better performance for image segmentation, especially for images corrupted by strong noise, compared to the traditional FCM and its extended methods.