A multimodal fusion method for Alzheimer's disease based on DCT convolutional sparse representation

Guo Zhang; Xixi Nie; Bangtao Liu; Hong Yuan; Jin Li; Weiwei Sun; Shixin Huang

doi:10.3389/fnins.2022.1100812

A multimodal fusion method for Alzheimer's disease based on DCT convolutional sparse representation

Front Neurosci. 2023 Jan 6:16:1100812. doi: 10.3389/fnins.2022.1100812. eCollection 2022.

Authors

Guo Zhang^{1

2}, Xixi Nie³, Bangtao Liu², Hong Yuan², Jin Li², Weiwei Sun⁴, Shixin Huang^{1

5}

Affiliations

¹ School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.
² School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.
³ Chongqing Key Laboratory of Image Cognition, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.
⁴ School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.
⁵ Department of Scientific Research, The People's Hospital of Yubei District of Chongqing City, Yubei, China.

Abstract

Introduction: The medical information contained in magnetic resonance imaging (MRI) and positron emission tomography (PET) has driven the development of intelligent diagnosis of Alzheimer's disease (AD) and multimodal medical imaging. To solve the problems of severe energy loss, low contrast of fused images and spatial inconsistency in the traditional multimodal medical image fusion methods based on sparse representation. A multimodal fusion algorithm for Alzheimer' s disease based on the discrete cosine transform (DCT) convolutional sparse representation is proposed.

Methods: The algorithm first performs a multi-scale DCT decomposition of the source medical images and uses the sub-images of different scales as training images, respectively. Different sparse coefficients are obtained by optimally solving the sub-dictionaries at different scales using alternating directional multiplication method (ADMM). Secondly, the coefficients of high-frequency and low-frequency subimages are inverse DCTed using an improved L1 parametric rule combined with improved spatial frequency novel sum-modified SF (NMSF) to obtain the final fused images.

Results and discussion: Through extensive experimental results, we show that our proposed method has good performance in contrast enhancement, texture and contour information retention.

Keywords: Alzheimer; convolutional; fusion; multimodal; sparse.