title: Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach.
abstract: This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is used to model the dependencies between affected regions of AD. The structure of relations between affected regions allows to detect neurodegeneration with an estimated performance of 88% on more than 400 subjects and predict neurodegeneration with 80% accuracy, supporting the conclusion that modeling the dependencies between components increases the recognition of different patterns of brain degeneration in AD.
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
authors.forename: Ignacio A
authors.initials: IA
authors.lastname: Illan
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: Javier
authors.initials: J
authors.lastname: Ramírez
authors.affiliation: Department of Scientific Computing, Florida State University Tallahassee, FL, USA.
authors.forename: Anke
authors.initials: A
authors.lastname: Meyer-Base
keywords: AD diagnosis; Bayesian networks; CAD systems; magnetic resonance imaging; spatial component analysis