A sum-modified-Laplacian and sparse representation based multimodal medical image fusion in Laplacian pyramid domain

Fusion of multimodal medical images provides complementary information for diagnosis, surgical planning, and clinical outcome evaluation. Although the multiscale decomposition-based fusion methods have attracted much attention among researchers, the challenges of determining the decomposition levels and the loss of contrast hindered their applications. Here, we present a multimodal medical images fusion method combining the sum-modified-Laplacian (SML) with sparse representation (SR) in the Laplacian pyramid domain. In this method, we first transformed the original images into the high-pass and low-pass bands by the Laplacian pyramid (LP). Then, we use SML and SR to fuse the high- and low-pass bands, respectively. The proposed method has been compared with different methods including NSST_VGG_MAX, DWT_ARV_BURTS, CVT_MAX_LIS, and NSCT_SR_MAX. We also conducted multiple experiments on four groups of medical images, including CT and MR, T1-weighted MR and T2-weighted MR, PET and MR, as well as SPECT and MR, to demonstrate the advantages of our method. Visual and quantitative results illustrate that our method can produce the fused images with better brightness contrast and retain more image details than other evaluated methods on the basis of MI, L^AB/F, Q^AB/F, and Q^w. Furthermore, our method could preserve more fine and useful functional information with better image contrast, which is highly relevant in the assessment of lesion shapes and positions. Graphical abstract The basic framework of the proposed fusion method based on sum-modified-Laplacian and sparse representation based in Laplacian pyramid domain using CT and MR images. In the proposed method, the source images are transformed into the low-pass bands and the high-pass bands using the Laplacian pyramid (LP). The low-pass bands are fused by SR, while SML is used to fuse the high-pass bands. Visual and quantitative results show that the proposed method can produce fused images with better brightness contrast and retain more image details than the other compared methods.