Regions of interest computed by SVM wrapped method for Alzheimer’s disease examination from segmented MRI Hidalgo-Muñoz, Antonio R. Ramírez Pérez De Inestrosa, Javier Gorriz Sáez, Juan Manuel Padilla De La Torre, Pablo Alzheimer’sdisease Gray and white matter Image segmentation MRI SVM 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. 2015-02-27T14:07:32Z 2015-02-27T14:07:32Z 2014 info:eu-repo/semantics/article Hidalgo-Muñoz, A.R.; et al. Regions of interest computed by SVM wrapped method for Alzheimer’s disease examination from segmented MRI. Frontiers in Aging Neuroscience, 6: 20 (2014). [http://hdl.handle.net/10481/35015] 1663-4365 http://hdl.handle.net/10481/35015 10.3389/fnagi.2014.00020 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Frontiers Foundation