Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/74259
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dc.contributor.authorBen Abdallah, Mariemen_US
dc.contributor.authorMalek, Jiheneen_US
dc.contributor.authorAzar, Ahmad Taheren_US
dc.contributor.authorMontesinos, Philippeen_US
dc.contributor.authorBelmabrouk, Hafedhen_US
dc.contributor.authorEsclarín Monreal, Julioen_US
dc.contributor.authorKrissian , Karlen_US
dc.date.accessioned2020-09-04T11:02:46Z-
dc.date.available2020-09-04T11:02:46Z-
dc.date.issued2015en_US
dc.identifier.issn1687-4188en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/74259-
dc.description.abstractWe propose an algorithm for vessel extraction in retinal images. The first step consists of applying anisotropic diffusion filtering in the initial vessel network in order to restore disconnected vessel lines and eliminate noisy lines. In the second step, a multiscale line-tracking procedure allows detecting all vessels having similar dimensions at a chosen scale. Computing the individual image maps requires different steps. First, a number of points are preselected using the eigenvalues of the Hessian matrix. These points are expected to be near to a vessel axis. Then, for each preselected point, the response map is computed from gradient information of the image at the current scale. Finally, the multiscale image map is derived after combining the individual image maps at different scales (sizes). Two publicly available datasets have been used to test the performance of the suggested method. The main dataset is the STARE project's dataset and the second one is the DRIVE dataset. The experimental results, applied on the STARE dataset, show a maximum accuracy average of around 94.02%. Also, when performed on the DRIVE database, the maximum accuracy average reaches 91.55%.en_US
dc.languageengen_US
dc.relation.ispartofInternational Journal of Biomedical Imagingen_US
dc.sourceInternational Journal of Biomedical Imaging [ISSN 1687-4188], Article ID 519024, (2015)en_US
dc.subject32 Ciencias médicasen_US
dc.subject33 Ciencias tecnológicasen_US
dc.subject.otherMatched-Filteren_US
dc.subject.otherDiabetic-Retinopathyen_US
dc.subject.otherImage-Analysisen_US
dc.subject.otherFundus Imagesen_US
dc.subject.otherSegmentationen_US
dc.titleAutomatic extraction of blood vessels in the retinal vascular tree using multiscale medialnessen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1155/2015/519024en_US
dc.identifier.isi000362066400001-
dc.identifier.eissn1687-4196-
dc.investigacionCiencias de la Saluden_US
dc.type2Artículoen_US
dc.contributor.daisngid2168641-
dc.contributor.daisngid34942898-
dc.contributor.daisngid241156-
dc.contributor.daisngid1256857-
dc.contributor.daisngid631655-
dc.contributor.daisngid3898650-
dc.contributor.daisngid1202623-
dc.description.numberofpages16en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Ben Abdallah, M-
dc.contributor.wosstandardWOS:Malek, J-
dc.contributor.wosstandardWOS:Azar, AT-
dc.contributor.wosstandardWOS:Montesinos, P-
dc.contributor.wosstandardWOS:Belmabrouk, H-
dc.contributor.wosstandardWOS:Monreal, JE-
dc.contributor.wosstandardWOS:Krissian, K-
dc.date.coverdate2015en_US
dc.identifier.ulpgces
dc.description.sjr0,557
dc.description.sjrqQ1
dc.description.esciESCI
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IUCES: Centro de Tecnologías de la Imagen-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.orcid0000-0003-1339-8700-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameEsclarín Monreal, Julio-
crisitem.author.fullNameKrissian , Karl-
Colección:Artículos
miniatura
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