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B. Magnin, L. Mesrob, S. Kinkingnéhun, et al., “Support Vector Machine-Based Classification of Alzheimer’s Disease from Whole-Brain Anatomical MRI,” Neuroradiology, Vol. 51, No. 2, 2009, pp. 73-83. doi:10.1007/s00234-008-0463-x

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• TITLE: DTI and Structural MRI Classification in Alzheimer’s Disease

  AUTHORS: Lilia Mesrob, Marie Sarazin, Valerie Hahn-Barma, Leonardo Cruz de Souza, Bruno Dubois, Patrick Gallinari, Serge Kinkingnéhun

  KEYWORDS: MRI; DTI; Multimodal; Classification; Alzheimer’s Disease

  JOURNAL NAME: Advances in Molecular Imaging, Vol.2 No.2, April 25, 2012

  ABSTRACT: In this paper, we propose a fully automated method to individually classify patients with Alzheimer’s disease (AD) and elderly control subjects based on diffusion tensor (DTI) and anatomical magnetic resonance imaging (MRI). We propose a new multimodal measure that combines anatomical and diffusivity measures at the voxel level. Our approach relies on whole-brain parcellation into 73 anatomical regions and the extraction of multimodal 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. Fifteen AD patients and 16 elderly controls were discriminated using mean diffusivity alone, combination of mean diffusivity and fractional anisotropy, and multimodal measures in the 73 ROIs and the overall accuracy obtained was 65.2%, 68.6% and 72% respectively. Overall accuracy reached 99% in multimodal measures when relevant regions were selected.