Image segmentation with PCNN model and maximum of variance ratio
结合最大方差比准则和PCNN模型的图像分割

The Pulse Coupled Neural Network (PCNN) model is very suitable for image segmentation. With given parameters, the results of segmentation are determined only by the times of iteration. However, the PCNN model itself cannot automatically discover the optimal iteration times. Therefore, an algorithm based on the maximization of variance ratio criteria is proposed to solve this problem. The algorithm can automatically discover the best iteration times by applying the maximization of variance ratio criteria, and get the best segmentation results. Eventually, the Shannon entropy rule is used to check the segmentation results. The experimental results show that the algorithm can automatically discover the optimal iteration times, the segmentation results are satisfactory, and it improves the speed of PCNN iteration, and it is also more efficient than the automatic segmentation algorithm based 2D-OTSU and cross-entropy.