In order to fully extract the complementary information between source images and improve the shortcomings of traditional image fusion algorithms in brightness maintenance,energy preservation and edge information preservation,a medical image fusion algorithm based on pulse coupled neural network (PCNN) image segmentation is proposed in this paper.The algorithm combines non-subsampled shearlet transform (NSST) and PCNN.Firstly,the source image with large standard deviation is selected as the segmented image and the source image with small standard deviation is used as the reference image.The source image is decomposed by NSST to obtain the low-frequency subband coefficients and high-frequency subband coefficients of the source image; In the low-frequency fusion,the parameter adaptive PCNN is used to segment the low-frequency subband of the segmented image,and the fused low-frequency subband coefficients are obtained according to the segmentation results; In high-frequency fusion,the product of regional energy and Laplace energy is used as the judgment function to obtain the fusion high-frequency subband coefficient; The fused image is obtained by inverse NSST transform.Finally,using the algorithm proposed in this paper,the fusion simulation of three groups of computerized tomography/magnetic resonance imaging (CT/MRI) images such as brain atrophy, acute stroke and hypertensive encephalopathy is carried out,and the simulation results are compared with the fusion images of five proposed algorithms in the international journal after 2018.The results show that the image obtained by using the fusion algorithm proposed in this paper effectively enhances the information complementarity between different modes, maintains the same brightness between the fused image and the source image,and retains the edge information of the low brightness part of the source image,which is more in line with the human visual characteristics and has higher objective evaluation indexes.