Diagnosis of Alzheimer’s Disease by Canonical Correlation Analysis Based Fusion of Multi-Modal Medical Images

Anahita Baninajjar, Hamid Soltanian-zadeh, Sajad Rezaie, Ali-Reza Mohammadi-Nejad

Abstract: In recent years, the number of people with Alzheimer's disease (AD) has grown worldwide. Since no cure has yet been found for the disease, detection and initiation of treatment is the best way to prevent the disease from progressing behavioral and physical symptoms in the patient. There are several methods to diagnose AD, and neuroimaging, as a noninvasive approach, reveals changes in the brain due to the disease. Diagnosis accuracy can be improved by fusion of various neuroimaging modalities. Canonical correlation analysis (CCA) and its extensions have been widely used for fusing multi-modal datasets, where healthy controls (HCs) are differentiated from patients by applying classification methods to canonical variables (CVs) resulting from CCA or its extensions. The goal of our study is to find an optimal method, from the perspective of accuracy and processing complexity, to diagnose patients with AD. This goal is achieved by fusing anatomical magnetic resonance imaging (MRI) and functional MRI (fMRI) data using CCA-based methods. Experimental results illustrate that HCs and AD patients can be classified using CVs obtained from structured and sparse CCA with greater than 90% accuracy.