Image thresholding segmentation based on two-dimensional minimum Tsallis-cross entropy

Abstract

Image thresholding segmentation method based on two-dimensional minimum Tsallis-cross entropy is proposed by utilizing the non-extensive property of Tsallis entropy in the paper. Firstly, the two-dimensional Tsallis-cross entropy is given, then the particle swarm optimization is used to search the best two-dimensional threshold vector by minimizing the two-dimensional Tsallis-cross entropy. The proposed method not only considers the spatial information of pixels, but also the interaction between the object and the background. Its segmentation performance is superior to thresholding methods using Shannon entropy and minimum one-dimensional Tsallis-cross entropy. Experimental results show that the proposed method can give good segmentation results with less computation time.