Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation

Shuaiqi Liu; Mingwang Wang; Lu Yin; Xiuming Sun; Yu-Dong Zhang; Jie Zhao

doi:10.3389/fncom.2021.803724

Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation

Front Comput Neurosci. 2022 Jan 31:15:803724. doi: 10.3389/fncom.2021.803724. eCollection 2021.

Authors

Shuaiqi Liu^{1

2

3}, Mingwang Wang^{1

2}, Lu Yin^{1

2}, Xiuming Sun³, Yu-Dong Zhang⁴, Jie Zhao^{1

2}

Affiliations

¹ College of Electronic and Information Engineering, Hebei University, Baoding, China.
² Machine Vision Technological Innovation Center of Hebei, Baoding, China.
³ School of Mathematics and Information Science, Zhangjiakou University, Zhangjiakou, China.
⁴ School of Computing and Mathematics, University of Leicester, Leicester, United Kingdom.

Abstract

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.

Keywords: CNN; bilateral filter; medical image fusion; scale decomposition; structure preservation.