Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/43833
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dc.contributor.authorFigueiredo, Isabel N.-
dc.contributor.authorKumar, Sunil-
dc.contributor.authorOliveira, Carlos M.-
dc.contributor.authorRamos, João Diogo-
dc.contributor.authorEngquist, Bjorn-
dc.date.accessioned2017-10-10T16:47:51Z-
dc.date.issued2015-
dc.identifier10.1016/j.compbiomed.2015.08.008-
dc.identifier.urihttp://hdl.handle.net/10316/43833-
dc.description.abstractDiabetic retinopathy (DR) is a sight-threatening condition occurring in persons with diabetes, which causes progressive damage to the retina. The early detection and diagnosis of DR is vital for saving the vision of diabetic persons. The early signs of DR which appear on the surface of the retina are the dark lesions such as microaneurysms (MAs) and hemorrhages (HEMs), and bright lesions (BLs) such as exudates. In this paper, we propose a novel automated system for the detection and diagnosis of these retinal lesions by processing retinal fundus images. We devise appropriate binary classifiers for these three different types of lesions. Some novel contextual/numerical features are derived, for each lesion type, depending on its inherent properties. This is performed by analysing several wavelet bands (resulting from the isotropic undecimated wavelet transform decomposition of the retinal image green channel) and by using an appropriate combination of Hessian multiscale analysis, variational segmentation and cartoon+texture decomposition. The proposed methodology has been validated on several medical datasets, with a total of 45,770 images, using standard performance measures such as sensitivity and specificity. The individual performance, per frame, of the MA detector is 93% sensitivity and 89% specificity, of the HEM detector is 86% sensitivity and 90% specificity, and of the BL detector is 90% sensitivity and 97% specificity. Regarding the collective performance of these binary detectors, as an automated screening system for DR (meaning that a patient is considered to have DR if it is a positive patient for at least one of the detectors) it achieves an average 95-100% of sensitivity and 70% of specificity at a per patient basis. Furthermore, evaluation conducted on publicly available datasets, for comparison with other existing techniques, shows the promising potential of the proposed detectors.por
dc.language.isoengpor
dc.publisherElsevierpor
dc.relationinfo:eu-repo/grantAgreement/FCT/COMPETE/132981/PTpor
dc.rightsembargoedAccess-
dc.subjectAlgorithmspor
dc.subjectAneurysmpor
dc.subjectAutomationpor
dc.subjectDatabases, Factualpor
dc.subjectDiabetic Retinopathypor
dc.subjectExudates and Transudatespor
dc.subjectHemorrhagepor
dc.subjectHumanspor
dc.subjectImage Interpretation, Computer-Assistedpor
dc.subjectImage Processing, Computer-Assistedpor
dc.subjectModels, Theoreticalpor
dc.subjectMultivariate Analysispor
dc.subjectPattern Recognition, Automatedpor
dc.subjectRetinapor
dc.subjectSensitivity and Specificitypor
dc.subjectWavelet Analysispor
dc.subjectFundus Oculipor
dc.titleAutomated lesion detectors in retinal fundus imagespor
dc.typearticle-
degois.publication.firstPage47por
degois.publication.lastPage65por
degois.publication.titleComputers in Biology and Medicinepor
dc.relation.publisherversionhttps://doi.org/10.1016/j.compbiomed.2015.08.008por
dc.peerreviewedyespor
dc.identifier.doi10.1016/j.compbiomed.2015.08.008por
degois.publication.volume66por
dc.date.embargo2018-10-10T16:47:51Z-
uc.controloAutoridadeSim-
item.fulltextCom Texto completo-
item.languageiso639-1en-
item.grantfulltextopen-
crisitem.author.deptFaculdade de Ciências e Tecnologia, Universidade de Coimbra-
crisitem.author.parentdeptUniversidade de Coimbra-
crisitem.author.researchunitCenter for Mathematics, University of Coimbra-
crisitem.author.orcid0000-0002-0215-8851-
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais
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