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Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:126-130.

FEATURE SELECTION IMPROVES THE ACCURACY OF CLASSIFYING ALZHEIMER DISEASE USING DIFFUSION TENSOR IMAGES.

Author information

1
Electronics & Computer Technology, Faculty of Technology, Gazi University, Ankara, Turkey ; Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, USA.
2
Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, USA.
3
Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
4
Department of Radiology, Medicine, and Psychiatry, University of California San Francisco, CA, USA ; Department of Veterans Affairs Medical Center, San Francisco, CA, USA.

Abstract

Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.

KEYWORDS:

Alzheimer’s disease; diffusion tensor imaging; fractional anisotropy; support vector machines; voxel-based analysis

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