Laplacian Redecomposition for Multimodal Medical Image Fusion
Xiaoxiao Li, Xiaopeng Guo, Pengfei Han, Xiang Wang, Huaguang Li, Tao Luo
Abstract
The field of multimodal medical image fusion has made huge progress in the past decade. However, previous methods always suffer from color distortion, blurring, and noise. To address these problems, we propose a novel Laplacian redecomposition (LRD) framework tailored to multimodal medical image fusion in this article. The proposed LRD has two technical innovations. First, we present a Laplacian decision graph decomposition scheme with image enhancement to obtain complementary information, redundant information, and low-frequency subband images. Second, considering the heterogeneous characteristics of redundant and complementary information, we introduce the concept of the overlapping domain (OD) and non-OD (NOD), where the OD contributes to fuse redundant information while the NOD is responsible for fusing complementary information. In addition, an inverse redecomposition scheme is given by leveraging the global decision graph and local mean to reconstruct high-frequency subband fusion images. Finally, the inverse Laplacian transform is applied to generate the fusion result. Experimental results demonstrate that the proposal outperforms other current popular fusion methods qualitatively and quantitatively.