Clustering based Image Segmentation via Weighted Fusion of Nonlocal and Local Information In this paper, we introduce a novel and effective clustering model combining the nonlocal and local information for the image segmentation. Recently, the nonlocal information has attracted much attention in the area of image processing for its excellent ability to handle the noise. Specifically, in this new model, we do an automatically weighted fusion of the nonlocal and local information of the image in the objective function of Kmeans. Thus, in the smoothing areas, the nonlocal information reduce the impact of noise in the region; and in the edge of regions, the local information help to keep the image details. The proposed model is a general optimization problem which can be solved by the iterative refinement technique like fuzzy cmeans or Kmeans, and it can automatically balance the contribution of nonlocal and local information. Verified by the experimental results on image segmentation, the proposed model is effective to improve the performance of clustering.

An improved fuzzy cmeans clustering algorithm with guided filter for Image Segmentation Fuzzy cmeans clustering with guided filter (FCMGF) is an effective method for noisy image segmentation. However, the parameter e of guided filter in the FCMGF is set to a fixed value, which weakens the ability of the FCMGF to partition images with different noise rates. In this paper, an improved fuzzy cmeans with guided filter method (FCMGF_I) is proposed. In our method, a new influence factor xcfx81 is defined to adjust the guidance image. By adjusting the value of xcfx81, the proposed FCMGF_I method achieves good performance on different noisy images. Experiments on Brain MR images show the superiority and efficiency of our method.