A medical image fusion method is proposed based on K-means Clustering and pulse coupled neural network (PCNN). Firstly,K-means Clustering method is used to segment the source image based on multi-feature information and the corresponding feature points are extracted,the multimodal medical image feature points are classified and merged to build feature points set. According to the distribution of feature points,the images are divided into texture regions and non-texture regions. The coefficients of texture region are input into PCNN and the ignition map is obtained;According to the ignition frequency,fusion coefficients are selected. The fusion coefficients of non-texture region are based on dual-channel PCNN. Experimental results show that the algorithm can accurately segment image texture region,and thus take advantage of dual-channel PCNN and PCNN in coefficient selection in different regions of the image. The proposed method gets a better image fusion result.