A novel method that combines ant colony optimization (ACO) and pulse coupled neu ral network (PCNN) for brain MRI image segmentation is proposed.The ACO is used to solve the problem of parameters setting for PCNN.The influence of low contrast and noise on image se gmentation is overcome and accurate segmentation can be achieved.Firstly,the sum of image information ent ropy and gray mean is used as the target function of ACO and the global search ability of ACO is used to set three key parameters of PCNN.Then the brain MRI image is segmented by the simp lified PCNN model which combines the maximum entropy criterion.Finally,the ultimate segment ation result is obtained via area filtering.Experimental results show that the proposed method can segment brain MRI image automatically and achieve relatively high accuracy and robustness.For images without noise,the average correct extraction rate of the method is above 97.0%,the average error extraction rate of it is below 0.4% and the average Jaccard similarity coefficient of it is above 94.8%.For images a dded with different levels of noise,segmentation results of the method are bette r than those of FCM and adaptive PCNN.