title: Regions of interest computed by SVM wrapped method for Alzheimer's disease examination from segmented MRI.
abstract: Accurate identification of the most relevant brain regions linked to Alzheimer's disease (AD) is crucial in order to improve diagnosis techniques and to better understand this neurodegenerative process. For this purpose, statistical classification is suitable. In this work, a novel method based on support vector machine recursive feature elimination (SVM-RFE) is proposed to be applied on segmented brain MRI for detecting the most discriminant AD regions of interest (ROIs). The analyses are performed both on gray and white matter tissues, achieving up to 100% accuracy after classification and outperforming the results obtained by the standard t-test feature selection. The present method, applied on different subject sets, permits automatically determining high-resolution areas surrounding the hippocampal area without needing to divide the brain images according to any common template.
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
authors.forename: Antonio R
authors.initials: AR
authors.lastname: Hidalgo-Muñoz
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Ramírez
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
authors.forename: Juan M
authors.initials: JM
authors.lastname: Górriz
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
authors.forename: Pablo
authors.initials: P
authors.lastname: Padilla
keywords: Alzheimer’s disease; MRI; SVM; gray and white matter; image segmentation