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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/34826

Title: Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach
Authors: Illan, Ignacio A.
Górriz Sáez, Juan Manuel
Ramírez Pérez de Inestrosa, Javier
Meyer-Base, Anke
Issue Date: 2014
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.
Sponsorship: This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andaluca, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103.
Publisher: Frontiers Foundation
Keywords: Bayesian networks
AD diagnosis
Spatial component analysis
Magnetic resonance imaging
CAD systems
URI: http://hdl.handle.net/10481/34826
ISSN: 1662-5188
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Illan, I.A.; et al. Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach. Frontiers in Computational Neuroscience, 26: 156 (2014). [http://hdl.handle.net/10481/34826]
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