Universidad de Granada Digibug
 

Repositorio Institucional de la Universidad de Granada >
1.-Investigación >
Departamentos, Grupos de Investigación e Institutos >
Departamento de Teoría de la Señal, Telemática y Comunicaciones >
DTSTC - Artículos >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/32023

Title: Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection
Authors: Ortiz García, Andrés
Górriz Sáez, Juan Manuel
Ramírez Pérez de Inestrosa, Javier
Martínez-Murcia, Francisco J.
Issue Date: 2014
Abstract: 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.
Sponsorship: 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) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRxResearch; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Publisher: Public Library of Science (PLOS)
Keywords: Alzheimer disease
Cognitive impairment
Database and informatics methods
Imaging techniques
Magnetic resonance imaging
Neuroimaging
Prototypes
Support vector machines
URI: http://hdl.handle.net/10481/32023
ISSN: 1932-6203
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: 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]
Appears in Collections:DTSTC - Artículos

Files in This Item:

File Description SizeFormat
Ortiz_StructuralBrain.pdf977.91 kBAdobe PDFView/Open
Recommend this item

This item is licensed under a Creative Commons License
Creative Commons

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! OpenAire compliant DSpace Software Copyright © 2002-2007 MIT and Hewlett-Packard - Feedback

© Universidad de Granada