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

• Published in: Frontiers in neuroscience Vol. 16; p. 1100812
• Main Authors: Zhang, Guo, Nie, Xixi, Liu, Bangtao, Yuan, Hong, Li, Jin, Sun, Weiwei, Huang, Shixin
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 06.01.2023
• Subjects: Alzheimer; convolutional; fusion; multimodal; Neuroscience; sparse; convolutional; fusion; multimodal; sparse; Alzheimer
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2022.1100812

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