Title:Alzheimer’s Disease Brain Areas: The Machine Learning Support for Blind Localization
VOLUME: 13 ISSUE: 5
Author(s):V. Vigneron, A. Kodewitz, A. M. Tome, S. Lelandais and E. Lang
Affiliation:IBISC, Equipe SIMOB, Universite ´ d’Evry, 40 rue du Pelvoux, 91020 Courcouronnes, France.
Keywords:AD, Alzheimer’s disease, classification, Computer-aided diagnosis, machine learning, MCI, PET scan, random
forest.
Abstract:The analysis of positron emission tomography (PET) scan image is challenging due to a
high level of noise and a low resolution and also because differences between healthy and demented
are very subtle. High dimensional classification methods based on PET have been proposed to automatically
discriminate between normal control group (NC) patients and patients with Alzheimer’s disease
(AD), with mild cognitive impairment (MCI), and mild cognitive impairment converting to Alzheimer’s
disease (MCIAD ) (a group of patients that clearly degrades to AD). We developed a voxelbased
method for volumetric image analysis. We performed 3 classification experiments AD vs CG, AD vs MCI, MCIAD
vs MCI. We will also give a small demonstration of the presented method on a set of face images. This method is capable
to extract information about the location of metabolic changes induced by Alzheimer’s disease that directly relies statistical
features and brain regions of interest (ROIs). We produce “maps” to visualize the most informative regions of the
brain and compare them with voxel-wise statistics. Using the mean intensity of about 2000 6 × 6 × 6mm patches, selected
by the extracted map, as input for a classifier we obtain a classification rate of 95.5%.
