Alzheimer'S Disease Classification Using Bag-Of-Words Based On Visual Pattern Of Diffusion Anisotropy For DTI Imaging.

Abstract

Diffusion tensor imaging (DTI) has recently been added to the large scale of studies for Alzheimer's Disease (AD) to investigate the White Matter (WM) defects that are not detectable using structural MRI. In this paper, we extracted Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) features, based on the visual diffusion patterns of Fractional Anisotropy (FA), and Mean Diffusivity (MD) maps, to build bag-of-words AD-signature for the hippocampal area. The experiments were accomplished with a subset of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset formed of AD patients (n = 35), Early Mild Cognitive Impairment (EMCI) (n=6), Late Mild Cognitive Impairment (LMCI) (n=24) and cognitively healthy elderly Normal Controls (NC) (n=31). The preliminary studied experiments give promising results that would consider the proposed system as an accurate and useful tool to capture the AD leanness with accuracy of 87% and 89% for FA and MD maps respectively.