According to the characterist ics of multi-scale transform,a novel medical image fusion algorithm based on human visual features and adaptive pulse coupled neural network (PCNN) is pro posed.Firstly,source images after registration are decomposed into low and high frequency sub-bands by nonsubsampled contourlet t ransform (NSCT).Secondly,majority ener gy and characteristics of the source image is retained in the low frequency sub-bands ,a fusion rule based on region energy (RE) combined with gradient singular value measurement (GSVM) is adopted.Moreover, considering the problem of global image feature,PCNN is utilized to fuse the high frequency sub-bands,the sum of local visual contrast (SLVC) to simulate the human visual feature is used as the external stimulus input to PCNN,the strength connection of PCNN is set to change with the visual contrast sensitivity (VCS),and considering th e iterations of PCNN,the Sigmoid function is used to compute the significant measurement of ignition output ampli tude of PCNN.Finally,the fused image is obtained by performing the inverse NSCT on the combined coefficients.T he comparison and analysis of experimental results show that the proposed approach can preserve in formation of source images effectively and improve the objective evaluation index and visual quality .