regions of interest computed by svm wrapped method for alzheimer’s disease examination from segmented mri

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ID: 246520
2014
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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 is proposed to be applied on segmented brain MRI for detecting the most discriminant AD regions of interest. The analyses are performed both on gray and white matter tissues, achieving up to 100 percent 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.
Reference Key
hidalgo-muoz2014frontiersregions Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Antonio R Hidalgo-Muñoz;Javier eRamírez;Juan M Górriz;Pablo ePadilla
Journal Frontiers in chemistry
Year 2014
DOI
10.3389/fnagi.2014.00020
URL
http://journal.frontiersin.org/Journal/10.3389/fnagi.2014.00020/full
Keywords
MRI white matter alzheimer’s disease image segmentation svm neuropsychiatry biological psychiatry

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