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Extracting MRS discriminant functional features of brain tumors

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Extracting MRS discriminant functional features of brain tumors

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dc.contributor.author Fuster García, Elíes es_ES
dc.contributor.author Tortajada Velert, Salvador es_ES
dc.contributor.author Vicente Robledo, Javier es_ES
dc.contributor.author Robles Viejo, Montserrat es_ES
dc.contributor.author García Gómez, Juan Miguel es_ES
dc.date.accessioned 2013-12-11T13:44:13Z
dc.date.available 2013-12-11T13:44:13Z
dc.date.issued 2013-05
dc.identifier.issn 0952-3480
dc.identifier.uri http://hdl.handle.net/10251/34457
dc.description.abstract The current challenge in automatic brain tumor classification based on MRS is the improvement of the robustness of the classification models that explicitly account for the probable breach of the independent and identically distributed conditions in the MRS data points. To contribute to this purpose, a new algorithm for the extraction of discriminant MRS features of brain tumors based on a functional approach is presented. Functional data analysis based on region segmentation (RSFDA) is based on the functional data analysis formalism using nonuniformly distributed B splines according to spectral regions that are highly correlated. An exhaustive characterization of the method is presented in this work using controlled and real scenarios. The performance of RSFDA was compared with other widely used feature extraction methods. In all simulated conditions, RSFDA was proven to be stable with respect to the number of variables selected and with respect to the classification performance against noise and baseline artifacts. Furthermore, with real multicenter datasets classification, RSFDA and peak integration (PI) obtained better performance than the other feature extraction methods used for comparison. Other advantages of the method proposed are its usefulness in selecting the optimal number of features for classification and its simplified functional representation of the spectra, which contributes to highlight the discriminative regions of the MR spectrum for each classification task. © 2012 John Wiley & Sons, Ltd. es_ES
dc.description.sponsorship The authors gratefully acknowledge former INTERPRET and eTUMOUR European project partners. Data providers: Professor B. Celda (Physical Chemistry, University of Valencia, Burjassot, Valencia, Spain); Dr F. A. Howe (St George's University of London, London, UK); Dr D. Monleon (Fundacion Investigacion HCUV-University of Valencia, Valencia, Spain); Professor A. Heerschap (Radboud University, Nijmegen, the Netherlands.); Dr. W. Gajewicz, Professor L. Stefanczyk and Dr J. Fortuniak (Uniwersytet Medycznyw Lodz, Lodz, Poland); Professor J. Griffiths (CR UK Cambridge Research Institute, Cambridge, UK); Professor A. C. Peet (Academic Department of Paediatrics and Child Health, University of Birmingham, Birmingham, UK); Professor W. Semmler (Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany); Dr. J. Calvar (Fundacion para la Lucha contra las Enfermedades Neurologicas de la Infancia, Buenos Aires, Argentina); Dr. J. Capellades (Hospital Universitari Germans Trias i Pujol, Badalona, Spain); and Dr. C. Majos (Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain). Data curators: Professor C. Arus, Dr M. Julia-Sape, Dr A. P. Candiota, Dr I. Olier, Ms T. Delgado, Ms J. Martin, Ms M. Camison and Mr A. Perez [all from Grup d'Aplicacions Biomediques de la Ressonancia Magestica Nuclear (GABRMN), Universitat Aut noma de Barcelona (UAB) (GABRMN-UAB) and CIBER-BBN]; and Professor B. Celda, Dra. M. C. Martinez-Bisbal and Dra. B. Martinez-Granados (all from Physical Chemistry, University of Valencia, Burjassot, Valencia, Spain). The authors would also like to thank Dr F. A. Howe and F. Raschke for their suggestions and comments. This work was partially funded by the European Commission: eTUMOUR (contract no. FP6-2002-LIFESCIHEALTH 503094), the HEALTHAGENTS EC project (HEALTHAGENTS) (contract no. FP6-2005-IST 027213). en_EN
dc.format.extent 15 es_ES
dc.language Inglés es_ES
dc.publisher Wiley-Blackwell es_ES
dc.relation.ispartof NMR in Biomedicine es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Brain tumors es_ES
dc.subject Clinical decision support systems es_ES
dc.subject Feature extraction es_ES
dc.subject MRS es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Extracting MRS discriminant functional features of brain tumors es_ES
dc.type Artículo es_ES
dc.embargo.terms forever
dc.identifier.doi 10.1002/nbm.2895
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/027214/EU/Agent-based Distributed Decision Support System for brain tumour diagnosis and prognosis/HEALTHAGENTS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/503094/EU/WEB ACCESSIBLE MR DECISION SUPPORT SYSTEM FOR BRAIN TUMOUR DIAGNOSIS AND PROGNOSIS, INCORPORATING IN VIVO AND EX VIVO GENOMIC AND METABOLIMIC DATA/ETUMOUR/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Fuster García, E.; Tortajada Velert, S.; Vicente Robledo, J.; Robles Viejo, M.; García Gómez, JM. (2013). Extracting MRS discriminant functional features of brain tumors. NMR in Biomedicine. 26(5):578-592. doi:10.1002/nbm.2895 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://onlinelibrary.wiley.com/doi/10.1002/nbm.2895/abstract
dc.description.upvformatpinicio 578 es_ES
dc.description.upvformatpfin 592 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 26 es_ES
dc.description.issue 5 es_ES
dc.relation.senia 234726
dc.contributor.funder European Commission es_ES
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