从目标和背景的类间差异性出发，提出了一种基于最大类间交叉熵准则的阈值化分割新算法，算法阈设目标的背景象素的条件分布服从正态分布，利用贝叶期公式估计象素属于目标和背景两类区域的后验概率，再搜索这两为区域后验概率之间的最大交叉熵。比较了新算法一基于最小交叉熵以及基于传统香农熵的阈值化算法的分割性能。

Although several image thresholding algorithms based on minimum cross entropy criterion have been proposed in recent years, only the form of the criterion or a priori probability and conditional probability was employed. In this paper, a new algorithm based on maximum between-class cross entropy using a posterior probability is presented for image thresholding taken into account the dissimilarity between object and background in image. Suppose the conditional distributions of object and background are modeled with normal distributions, the a posterior probabilities are computed by Bayes formula. The new algorithm is compared with a number of traditional algorithms based on Shannon entropy and minimum cross entropy by applying them to various test images.