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dc.contributor.authorMorales Martínez, Sandra 
dc.contributor.authorNaranjo Ornedo, Valeriana 
dc.contributor.authorAngulo, Jesús 
dc.contributor.authorLegaz Aparicio, Alvar Ginés 
dc.contributor.authorVerdú Monedero, Rafael 
dc.date.accessioned2020-01-22T09:04:21Z
dc.date.available2020-01-22T09:04:21Z
dc.date.issued2017-03-23
dc.identifier.citationMorales, S.; Naranjo Ornedo, V.; Angulo, J.; Legaz-Aparicio, A.; Verdu-Monedero, R. (2017). Retinal network characterization through fundus image processing: Significant point identification on vessel centerline. Signal Processing: Image Communication. 59:50-64. https://doi.org/10.1016/j.image.2017.03.013es_ES
dc.identifier.issn0923-5965
dc.description.abstractThis paper describes a new approach for significant point identification on vessel centerline. Significant points such as bifurcations and crossovers are able to define and characterize the retinal vascular network. In particular, hit-or-miss transformation is used to detect terminal, bifurcation and simple crossing points but a post-processing stage is needed to identify complex intersections. This stage focuses on the idea that the intersection of two vessels creates a sort of close loop formed by the vessels and this effect can be used to differentiate a bifurcation from a crossover. Experimental results show quantitative improvements by increasing the number of true positives and reducing the false positives and negatives in the significant point detection when the proposed method is compared with another state-of-the-art work. A sensitivity equal to 1 and a predictive positive value of 0.908 was achieved in the analyzed cases. Hit-or-miss transformation must be applied on a binary skeleton image. Therefore, a method to extract the vessel skeleton in a direct way is also proposed. Although the identification of the significant points of the retinal tree can be useful by itself for multiple applications such as biometrics and image registration, this paper presents an algorithm that makes use of the significant points to measure the bifurcation angles of the retinal network which can be related to cardiovascular risk determination.es_ES
dc.description.sponsorshipThis work was supported by the Ministerio de Economia y Competitividad Spain, Project ACRIMA (TIN2013-46751-R). The authors would like to thank people who provide the public databases used in this work (DRIVE, STARE and VARIA).es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationEstimación de la orientación múltiple mediante un banco de filtros y su uso en el desarrollo de aplicaciones de procesado de imagenes_ES
dc.relation.urihttp://hdl.handle.net/10317/8312es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights© Copyright 2017 Elsevieres_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleRetinal network characterization through fundus image processing: significant point identification on vessel centerlinees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.otherTeoría de la Señal y las Comunicacioneses_ES
dc.subjectRetinal skeletones_ES
dc.subjectVessel centerlinees_ES
dc.subjectSignificant pointses_ES
dc.subjectBifurcationses_ES
dc.subjectCrossingses_ES
dc.subjectBifurcation angleses_ES
dc.identifier.urihttp://hdl.handle.net/10317/8319
dc.identifier.url10.1016/j.image.2017.03.013
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/submittedVersiones_ES
dc.relation.projectIDMINECO/TIN2013-46751-R-ARes_ES
dc.subject.unesco2209.90 Tratamiento Digital. Imágeneses_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.image.2017.03.013es_ES


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