The multimodal medical images fusion is useful in the field of medical sciences. The main aim is to obtain the applicable information from the medical image sources and fuse them together to provide a single output which forms as an important system in the medical diagnosis. The fusion criterion is to minimize different error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the interested objects is more important than other information. Therefore, how to preserve the edge-like features is worthy of investigating for medical image fusion. In term of this view, the project proposed a new medical image fusion scheme based on discrete contourlet transformation and pulse couple neural network   , which is useful to provide more details about edges at curves. It is used to improve the edge information of fused image by reducing the distortion. The pixel and decision level fusion rule will be applied selected for low frequency and high frequency. The fused contourlet coefficients are reconstructed by inverse NS contourlet transformation. The goal of image fusion is to obtain useful complementary information from CT/MRI multimodality images.  By this method we can get more complementary information.