Threshold Selection Based on Fuzzy Tsallis Entropy and Particle Swarm Optimization

Tsallis entropy is a generalization of Shannon entropy and can describe physical system with long range interactions, long time memories and fractal-type structures. In this paper, a novel threshold selection technique for image segmentation is proposed by combining Tsallis entropy and fuzzy c-partition. The image to be segmented is firstly transformed into fuzzy domain using membership function. Then, the fuzzy Tsallis entropies for object and background are defined. The threshold is selected by finding a proper parameter combination of membership function such that the total fuzzy Tsallis entropy is maximized. To reduce the computational complexity, particle swarm optimization (PSO) is used to search the optimal parameter combination. The main advantage of the proposed method is that it considers not only the information of object and background but also interactions between them in threshold selection procedure. Experimental results show that the proposed method can give better segmentation performance than methods based on traditional Shannon entropy.

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