Aiming at the problems of blurred details, incomplete energy preservation and long running time in traditional medical image fusion,a medical image fusion method based on hybrid filtering and improved edge detection pulse coupled neural network (PCNN) in non-subsampled shearlet transform (NSST) domain is proposed.Firstly,the YUV model is used to perform a color space conversion to separate the luminance channel Y,and then compound filter is used to enhance the source MRI image and the grayscale image of the luminance channel in different degrees.Secondly,the grayscale images of the enhanced magnetic resonance imaging (MRI) and luminance channels are decomposed using NSST to obtain the high and low frequency subbands.The low-frequency subband uses a fusion strategy with a modified Laplace energy sum and a local area energy weighted sum,and the high-frequency subband uses an improved edge detection PCNN fusion strategy.Finally,the fused images are obtained by NSST inverse transformation.By comparing with other six fusion methods,this method can effectively improve the detail extraction and energy preservation in the process of image fusion,and the overall algorithm operates with high efficiency and good visibility.