Mostrar el registro sencillo del ítem
dc.contributor.author | López-Mir, Fernando | es_ES |
dc.contributor.author | Naranjo Ornedo, Valeriana | es_ES |
dc.contributor.author | Angulo, J. | es_ES |
dc.contributor.author | Alcañiz Raya, Mariano Luis | es_ES |
dc.contributor.author | Luna, L. | es_ES |
dc.date.accessioned | 2015-07-03T10:11:12Z | |
dc.date.available | 2015-07-03T10:11:12Z | |
dc.date.issued | 2014-04 | |
dc.identifier.issn | 0169-2607 | |
dc.identifier.issn | 1872-7565 | |
dc.identifier.uri | http://hdl.handle.net/10251/52669 | |
dc.description.abstract | There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 +/- 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI. (C) 2014 Elsevier Ireland Ltd. All rights reserved. | es_ES |
dc.description.sponsorship | This work has been supported by the MITYC under the project NaRALap (ref. TSI-020100-2009-189), partially by the CDTI under the project ONCOTIC (IDI-20101153), by Ministerio de Educacion y Ciencia Spain, Project Game Teen (TIN2010-20187) projects Consolider-C (SEJ2006-14301/PSIC), "CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII" and Excellence Research Program PROMETEO (Generalitat Valenciana. Conselleria de Educacion, 2008-157). We would like to express our gratitude to the Hospital Clinica Benidorm, for providing the MR datasets and to the radiologist team of Inscanner for the manual segmentation of the MR images. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Computer Methods and Programs in Biomedicine | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Magnetic resonance imaging | es_ES |
dc.subject | Liver segmentation | es_ES |
dc.subject | Mathematical morphology | es_ES |
dc.subject | Stochastic partitions | es_ES |
dc.subject | Watershed transform | es_ES |
dc.subject.classification | TEORIA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.subject.classification | EXPRESION GRAFICA EN LA INGENIERIA | es_ES |
dc.title | Liver segmentation in MRI: a fully automatic method based on stochastic partitions | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.cmpb.2013.12.022 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MITURCO//TSI-020100-2009-0189/ES/SISTEMA DE NAVEGACIÓN MEDIANTE REALIDAD VIRTUAL EN CIRUGÍA LAPAROSCOPICA/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//IDI-20101153/ES/TERAPIAS ASISTIVAS COLABORATIVAS PARA EL TRATAMIENTO ONCOLÓGICO MEDIANTE EL USO DE TECNOLOGÍAS TIC - ONCOTIC/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TIN2010-20187/ES/ENTORNOS INMERSIVOS Y PERSUASIVOS PARA LA EVALUACION Y ENTRENAMIENTO DE ESTRATEGIAS DE REGULACION EMOCIONAL. APLICACION A LA EDUCACION PSICOSOCIAL EN ADOLESCENTES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MEC//SEJ2006-14301/ES/NUEVAS TECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION: INTEGRACION Y CONSOLIDACION DE SU USO EN CIENCIAS SOCIALES PARA MEJORAR LA SALUD, LA CALIDAD DE VIDA Y EL BIENESTAR./ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEO08%2F2008%2F157/ES/Promoción del bienestar a través de las tecnologías de la información y comunicación (probientic)/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica | es_ES |
dc.description.bibliographicCitation | López-Mir, F.; Naranjo Ornedo, V.; Angulo, J.; Alcañiz Raya, ML.; Luna, L. (2014). Liver segmentation in MRI: a fully automatic method based on stochastic partitions. Computer Methods and Programs in Biomedicine. 114(1):11-28. https://doi.org/10.1016/j.cmpb.2013.12.022 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.cmpb.2013.12.022 | es_ES |
dc.description.upvformatpinicio | 11 | es_ES |
dc.description.upvformatpfin | 28 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 114 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.senia | 260322 | |
dc.contributor.funder | Ministerio de Educación y Ciencia | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Ministerio de Industria, Turismo y Comercio | es_ES |
dc.contributor.funder | Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición | es_ES |