To improve the segmentation performance of thresholding methods, a novel strategy of integrating the spatial information between pixel's is proposed in this paper. The proposed strategy utilizes pixel's gray level and its local entropy within a neighborhood to construct a novel 2-D histogram, called gray level-local entropy (GLLE) histogram. The local entropy can effectively reflect the homogeneity of a pixel's gray level in a neighborhood. Based on the GLLE histogram, an ideal thresholding vector is obtained by maximizing the total Tsallis entropy of background and objects. The proposed method is validated through segmenting several real images. Experimental results show that the proposed method outperforms many existing thresholding methods.
