Features-based approach for Alzheimer's disease diagnosis using visual pattern of water diffusion in Tensor Diffusion Imaging

In this paper, we propose a feature-based classification framework for Alzheimer’s disease (AD) recognition using Tensor Diffusion Imaging (DTI). The main contribution consists in considering the visual pattern of water molecules diffusion in the most involved region in AD (hippocampal area). We use the Circular Harmonic Functions (CHFs) and the Bag-of-Visual-Words approach to build an AD related-signature. The experiments were accomplished first with a subset of participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and then with the DTI scans of a French epidemiological study: ”Bordeaux-3City”. Experimental results demonstrate that our features-based method applied on the MD maps is able to capture the AD-related atrophy and then classify between AD subjects.