为了自动地进行图像的多值分割，从原始图像与分割图像之间的相互关系出发，以最大互信息为优化分割目标，以互信息熵差作为一种新的分类类数判据，在对传统脉冲耦合神经网络模型改进的基础上，提出了一种基于最大互信息改进型脉冲耦合神经网络图像多值分割算法.理论分析和实验结果表明，该方法能够自动确定最佳分割迭代次数及最佳分割灰度类数，对分割图像具有良好的特征划分能力，且在分割类数较少的情况下，能较好地保持图像细节、纹理及边缘等信息，对不同图像分割准确度高，具有较强的适用性.

In order to process multi-threshold image segmentation automatically,the traditional pulse coupled neural networks model is improved.A new algorithm of multi-threshold image segmentation using improved PCNN based on the maximization of mutual information is put forward according to the relationship between original image and segmented image,which is based on the optimization object of maximal of mutual information and a new measurement criterion for determining the number of clusters in an image called difference of mutual information.Theoretical analysis and simulation results indicate that the new method can automatically determine the optimal cyclic iterative times and the optimal number of gray-scale clusters,has a favorable capability to carve up characteristics and maintain the edges,texture and details of images,has higher precision in different image segmentation and can be more adaptability.