Identification of Atrophy Patterns in Alzheimer's Disease Based on SVM Feature Selection and Anatomical Parcellation

In this paper, we propose a fully automated method to individually classify patients with Alzheimer’s disease (AD) and elderly control subjects based on anatomical magnetic resonance imaging (MRI). Our approach relies on the identification of gray matter (GM) atrophy patterns using whole-brain parcellation into anatomical regions and the extraction of GM characteristics in these regions. Discriminative features are identified using different feature selection (FS) methods and used in a Support Vector Machine (SVM) for individual classification. We compare two different types of parcellations corresponding to two different levels of anatomical details. We validate our approach with two distinct groups of subjects: an initial cohort of 16 AD patients and 15 elderly controls and a second cohort of 17 AD patients and 13 controls. We used the first cohort for training and region selection and the second cohort for testing and obtained high classification accuracy (90%).