Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48801
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dc.contributor.authorRekhi, Ravitej Singhen_US
dc.contributor.authorIssac, Ashishen_US
dc.contributor.authorDutta, Malay Kishoreen_US
dc.contributor.authorTravieso, Carlos M.en_US
dc.date.accessioned2018-11-24T01:03:10Z-
dc.date.available2018-11-24T01:03:10Z-
dc.date.issued2017en_US
dc.identifier.isbn9781538608500en_US
dc.identifier.urihttp://hdl.handle.net/10553/48801-
dc.description.abstractDiabetic Retinopathy and Diabetic Macular Edema are diseases that affect vision and eventually may lead to blindness. Early detection is a must to prevent the progression of the disease imploring the need for effective computer-aided diagnostic techniques. In the following research paper, a robust method has been proposed to segment hard exudates from digital, color fundus images using anisotropic diffusion and adaptive thresholding followed by a support vector machine for classification. The geometrical, shape and orientation features have been used to correctly classify the segmented objects as exudates or false pixels. The proposed technique has a high specificity and eliminates false positives correctly when applied across a wide range of images. The exudates segmented have a high degree of accuracy and no false positives are generated in case of non-diseased images. The proposed method has been tested on a total 189 images of the DIARETDB1 and MESSIDOR database and achieves an accuracy of 92.13% and 90% respectively. The proposed method can be used in the development for some computer aided technology for ocular diseases detection from fundus images.en_US
dc.languageengen_US
dc.relation.ispartof2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedingsen_US
dc.source2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedings (7985527)en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherMedical Imagingen_US
dc.subject.otherDiabetic Macular Edemaen_US
dc.subject.otherFundus imageen_US
dc.subject.otherExudatesen_US
dc.subject.otherAnisotropic Diffusionen_US
dc.subject.otherSVMen_US
dc.titleAutomated classification of exudates from digital fundus imagesen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.identifier.doi10.1109/IWOBI.2017.7985527en_US
dc.identifier.scopus85028562476-
dc.contributor.authorscopusid57195518313-
dc.contributor.authorscopusid56800652200-
dc.contributor.authorscopusid35291803600-
dc.contributor.authorscopusid6602376272-
dc.identifier.issue7985527-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.utils.revisionen_US
dc.identifier.ulpgces
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptIDeTIC: 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-
Appears in Collections:Actas de congresos
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