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.