An Image Segmentation Method by Multiscale Local Thresholding Based on Class Uncertainty Theory xe2x88x97 Image segmentation is one of the most important core technologies in the field of image processing and computer vision, which has lots of applications specifically in medical image analysis. Due to medical imaging mechanism, medical images usually suffer from heavy noises, uneven intensity distribution and fuzzy boundaries between biological tissues. That makes classical segmentation methods based on global or local thresholding difficult to get accurate results. This paper proposes an image segmentation method by multiscale local thresholding based on class uncertainty theory. Firstly, the original image is divided into a set of subregions with different scales by using a multilayer pyramid structure. Secondly, the energy function with inequality constraints is constructed based on class uncertainty and region uniformity. Then by an iterative process, the optimal local threshold for each subregion are calculated by an optimization algorithm for each layer until the final optimal threshold mask is determined for image segmentation. Experimental results verify that the proposed method can effectively eliminate the interferences caused by image noises, intensity unevenness and fuzzy boundaries, and preserve details of object structure while segmenting the object from the background. Segmentation results demonstrate the superior performance of the proposed method by comparison with supervised rangeconstrained thresholding methods (RCOtsu), classical globallocal Otsu method, and the minimization of homogeneity and uncertaintybased energy method (MHUE).

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.