Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection Ortiz García, Andrés Gorriz Sáez, Juan Manuel Ramírez Pérez De Inestrosa, Javier Martínez Murcia, Francisco Jesús Alzheimer disease Cognitive impairment Database and informatics methods Imaging techniques Magnetic resonance imaging Neuroimaging Prototypes Support vector machines This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients. 2014-05-29T08:15:11Z 2014-05-29T08:15:11Z 2014 info:eu-repo/semantics/article Ortiz, A.; et al. Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection. Plos One, 9(4): e93851 (2014). [http://hdl.handle.net/10481/32023] 1932-6203 http://hdl.handle.net/10481/32023 1371/journal.pone.0093851 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Public Library of Science (PLOS)