基于二维最小Tsallis交叉熵的图像阈值分割方法

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

利用Tsallis熵的非广延性,提出了二维最小Tsallis交叉熵阈值分割方法.首先给出了二维Tsallis交叉熵的定义,并以最小二维Tsallis交叉熵为准则,利用粒子群优化算法来搜索最优二维阈值向量.该方法不仅进一步考虑了像素之间的空间邻域信息,而且考虑了目标和背景之间的相互关系,其分割性能优于基于Shannon熵的交叉熵阈值法和一维最小Tsallis交叉熵阈值法,并且具有很强的抗噪声能力.实验结果表明,该方法可以实现快速、准确的分割.

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.