dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Rouco, J. | |
dc.contributor.author | Penedo, Manuel | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-06-07T14:20:41Z | |
dc.date.available | 2024-06-07T14:20:41Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | de Moura, J., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2017). "Automatic vessel detection by means of brightness profile characterization in OCT images" in Knowledge Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference, KES-2017. Procedia computer science, 112, 980-988. https://doi.org/10.1016/j.procs.2017.08.142. | es_ES |
dc.identifier.issn | 1877-0509 | |
dc.identifier.uri | http://hdl.handle.net/2183/36841 | |
dc.description.abstract | [Abstract]: Optical Coherence Tomography (OCT) is a well-established medical imaging technique that allows the analysis of the eye fundus characteristics in real time. These images enable the experts to make a clinical evaluation of the retinal vasculature, whose morphology provides relevant information for diseases like diabetes, hypertension or arteriosclerosis. In this paper, we present a novel proposal for the automatic vasculature identification in retinal OCT images. To achieve this, we analyse the intensity profiles between representative retinal layers, previously segmented. Then, two statistical models are generated using representative samples of vessel and non-vessel profiles. The analysis of both statistical models let us optimize the discrimination of both cathegories that is used, finally, to identify the vessel locations. The proposed method was adjusted and validated using 256 OCT images, including 1274 vascular structures that were labelled by an expert clinician. Satisfactory results were provided as a precision of 94.55% and a recall of 90.25% were obtained, respectively. The method facilitates the doctors’ work allowing better analysis and treatments of vascular diseases. | es_ES |
dc.description.sponsorship | This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier B.V. | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2015-69948-R/ES/IDENTIFICACION Y CARACTERIZACION DEL EDEMA MACULAR DIABETICO MEDIANTE ANALISIS AUTOMATICO DE TOMOGRAFIAS DE COHERENCIA OPTICA Y TECNICAS DE APRENDIZAJE MAQUINA | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PI14%2F02161/ES/DESARROLLO DE UN SISTEMA AUTOMÁTICO PARA EL CÁLCULO Y VISUALIZACIÓN DE PROPIEDADES ANATÓMICAS DE LA RETINA EN SD-OCT Y SU CORRELACIÓN CON ANÁLISIS FUNCIONALES HETEROGÉNEOS DE LA VISIÓN | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DTS15%2F00153/ES/SIRIUS - SISTEMA DE ANÁLISIS DE MICROCIRCULACIÓN RETINIANA: EVALUACIÓN MULTIDISCIPLINAR E INTEGRACIÓN EN PROTOCOLOS CLÍNICOS | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.procs.2017.08.142 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Computer-aided diagnosis | es_ES |
dc.subject | Retinal imaging | es_ES |
dc.subject | Optical Coherence Tomography | es_ES |
dc.subject | Vessel detection | es_ES |
dc.title | Automatic vessel detection by means of brightness profile characterization in OCT images | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Procedia Computer Science | es_ES |
UDC.volume | 112 | es_ES |
UDC.startPage | 980 | es_ES |
UDC.endPage | 988 | es_ES |
dc.identifier.doi | 10.1016/j.procs.2017.08.142 | |
UDC.conferenceTitle | International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES 2017 | es_ES |