@misc{10481/35015,
year = {2014},
url = {http://hdl.handle.net/10481/35015},
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.},
organization = {This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904).},
organization = {This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.},
publisher = {Frontiers Foundation},
keywords = {Alzheimer’sdisease},
keywords = {Gray and white matter},
keywords = {Image segmentation},
keywords = {MRI},
keywords = {SVM},
title = {Regions of interest computed by SVM wrapped method for Alzheimer’s disease examination from segmented MRI},
doi = {10.3389/fnagi.2014.00020},
author = {Hidalgo-Muñoz, Antonio R. and Ramírez Pérez De Inestrosa, Javier and Gorriz Sáez, Juan Manuel and Padilla De La Torre, Pablo},
}