Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation

Medical image fusion has an indispensable value in the medical field. Taking advantage of structure-preserving filter and deep learning, a structure preservation-based two-scale multimodal medical image fusion algorithm is proposed. First, we used a two-scale decomposition method to decompose source images into base layer components and detail layer components. Second, we adopted a fusion method based on the iterative joint bilateral filter to fuse the base layer components. Third, a convolutional neural network and local similarity of images are used to fuse the components of the detail layer. At the last, the final fused result is got by using two-scale image reconstruction. The contrast experiments display that our algorithm has better fusion results than the state-of-the-art medical image fusion algorithms.

Automated summary

Medical image fusion algorithm based on structure preservation and deep learning is proposed in this study. The algorithm decomposes the source images into base layer components and detail layer components using a two-scale decomposition method. It then utilizes an iterative joint bilateral filter and a convolutional neural network to fuse the components of the base layer and detail layer, respectively. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art techniques in medical image fusion.