Medical image fusion has very important application value for medical image analysis and diseases diagnosis.According to the characteristics of multi modality medical image and human visual features

a new medical image fusion algorithm in NSCT (nonsubsampled coutourlet

NSCT) domain is proposed.Firstly

source images after registration are decomposed into low and high frequency sub-bands using NSCT.According to the low frequency subbands concentrating the majority energy of the source image and determining the image coutour

a fusion rule based on weighted region average energy combined with average gradient is adopted in low frequency subband coefficients.Moreover

according to human visual system which is more sensitive to contrast and edge

texture of image

the fusion strategy based on directive contrast integrated with the improved energy of Laplacian and PCNN (Pulse Coupled Neural Network

PCNN) are used to fuse high-frequency subbands.Furthermore

a closed loop feedback is introduced into the fusion rules of low and high frequency subbands to obtain optimal fused weights adaptively by using WSSIM (Weighted Structure Similarity

WSSIM) which highly consistent with the HVS(human visual features

HVS) as objective function.Finally

a lot of experiments of fusion of images including gray images and color images based on different fusion methods are conducted.The experiment results are analyzed in terms of visual quality and objective evaluation.The experiment results show that the proposed algorithm can effectively preserve information and significantly improve the performance of fusion image in terms of quantity of information

dispersed gray scale

visual quality and objective evaluation index.