Hybrid Multi-Scale Medical Image Fusion Based on Structural Similarity Optimization
Existing multi-modal medical image fusion methods suffer from incomplete preservation of structural information and phase features.Therefore,this study proposes a medical image fusion method based on hybrid multi-scale decomposition and local structure similarity optimization.First,the proposed hybrid multi-scale decomposition method addresses limitations of a single filter in preserving the structure and details of an image.This method combines Multi-level Decomposition Latent Low-Rank Representation(MDLatLRR)and NonSubsampled Contourlet Transform(NSCT).MDLatLRR decomposes the source image into low-rank and salient layers,and NSCT further decomposes the low-rank layer.Second,a fusion rule based on the local Laplacian energy sum applied to the base layer improves the visual effects of the fused image.A Pulse-Coupled Neural Network(PCNN)calculates the global coupling to obtain the fusion weight to fuse the detail layer.Finally,considering spatial consistency,linearly adjusted images are obtained from the initial fusion image using the weighted local structure similarity index measure to determine the correction coefficient.The accuracy of the information in the fusion image improves by correcting the initial fusion.Experiments demonstrate that compared with nine other methods such as MSMG,EMFusion,and CFL,the proposed method has a higher evaluation performance in ten objective evaluation indexes such as the normalized mutual information and spatial frequency error ratio.The average improvements in phase consistency,cosine features mutual information,and the sum of the correlations of differences are 13.89%,19.62%,and 35.8%,respectively,compared with those in the second method.The fused image has better visual effects and richer,more accurate,and more detailed information.
国家科技期刊平台
NSTL
万方数据
