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Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69793
DC FieldValueLanguage
dc.contributor.authorGupta, Varunen_US
dc.contributor.authorSengar, Namitaen_US
dc.contributor.authorDutta, Malay Kishoreen_US
dc.contributor.authorTravieso González, Carlos Manuelen_US
dc.date.accessioned2020-02-05T12:50:06Z-
dc.date.accessioned2020-06-23T08:53:35Z-
dc.date.available2020-02-05T12:50:06Z-
dc.date.available2020-06-23T08:53:35Z-
dc.date.issued2018en_US
dc.identifier.isbn9781614999287en_US
dc.identifier.issn0922-6389en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/69793-
dc.description.abstractIn this paper an algorithm is proposed for detection of severity of Diabetic Macular Edema (DME) from Retinal Images, which are invariant to image rotation. The proposed work includes detection of optic disc, macula, exudates, region of interest localization and a level indicator which indicates the severity of disease as severe, moderate or normal DME. Rotation Invariant detection of macula makes this method efficient. A set of NPDR data of fundus image of an eye is used to test the proposed algorithm. The results show a better comprehensive performance of disease severity checker and computationally efficient.en_US
dc.languageengen_US
dc.relation.ispartofFrontiers in Artificial Intelligence and Applicationsen_US
dc.sourceFrontiers in Artificial Intelligence and Applications [ISSN 0922-6389], v. 310, p. 336-344en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherDiabetic Macular Edemaen_US
dc.subject.otherExudatesen_US
dc.subject.otherMaculaen_US
dc.subject.otherMorphologicalen_US
dc.subject.otherOptic Discen_US
dc.subject.otherRotation Invarianten_US
dc.subject.otherThresholdingen_US
dc.titleAdaptive threshold based automated identification of severity of diabetic macular edema from retinal imagesen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.relation.conference1st International Conference on Applications of Intelligent Systems, APPIS 2018en_US
dc.identifier.scopus85059614204-
dc.contributor.authorscopusid57193866196-
dc.contributor.authorscopusid56964145800-
dc.contributor.authorscopusid35291803600-
dc.contributor.authorscopusid57201316633-
dc.description.lastpage344en_US
dc.description.firstpage336en_US
dc.relation.volume310en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Capítulo de libroen_US
dc.utils.revisionSíen_US
dc.identifier.conferenceidevents121647-
dc.identifier.ulpgcSíes
dc.description.sjr0,19
dc.description.sjrqQ4
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
crisitem.event.eventsstartdate10-01-2018-
crisitem.event.eventsenddate12-01-2018-
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