Aiming at the serious loss of details and poor visual effect in the process of medical image fusion, a pulse coupled neural network (PCNN) medical image fusion algorithm based on non-subsampled contourlet transform (NSCT) and discrete wavelet transform (DWT) is proposed. Firstly, the medical source image is processed by NSCT to obtain the corresponding low frequency and high frequency subbands, and the obtained low frequency subbands are processed by DWT. Then, the PCNN is used to fuse the low frequency subbands, where the input items are the average gradient and the improved Laplacian energy sum. The fusion of high frequency subbands is realized by combining information entropy and matching degree. Finally, the low frequency subband image and high frequency subband image are fused by multi-scale inverse transformation. Experimental results show that the proposed method can effectively improve the contrast of the fused image and retain the detailed information of the source image, and has excellent performance in both subjective and objective evaluation.