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dc.contributor.authorGao, Yongshengen_US
dc.contributor.authorLam, Bensonen_US
dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.date.accessioned2017-04-04T17:13:47Z
dc.date.available2017-04-04T17:13:47Z
dc.date.issued2010en_US
dc.date.modified2011-02-09T06:41:48Z
dc.identifier.issn02780062en_US
dc.identifier.doi10.1109/TMI.2010.2043259en_AU
dc.identifier.urihttp://hdl.handle.net/10072/35458
dc.description.abstractDetecting blood vessels in retinal images with the presence of bright and dark lesions is a challenging unsolved problem. In this paper, a novel multiconcavity modeling approach is proposed to handle both healthy and unhealthy retinas simultaneously. The differentiable concavity measure is proposed to handle bright lesions in a perceptive space. The line-shape concavity measure is proposed to remove dark lesions which have an intensity structure different from the line-shaped vessels in a retina. The locally normalized concavity measure is designed to deal with unevenly distributed noise due to the spherical intensity variation in a retinal image. These concavity measures are combined together according to their statistical distributions to detect vessels in general retinal images. Very encouraging experimental results demonstrate that the proposed method consistently yields the best performance over existing state-of-the-art methods on the abnormal retinas and its accuracy outperforms the human observer, which has not been achieved by any of the state-of-the-art benchmark methods. Most importantly, unlike existing methods, the proposed method shows very attractive performances not only on healthy retinas but also on a mixture of healthy and pathological retinas.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent525887 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherI E E Een_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom1369en_US
dc.relation.ispartofpageto1381en_US
dc.relation.ispartofissue7en_AU
dc.relation.ispartofjournalI E E E Transactions on Medical Imagingen_US
dc.relation.ispartofvolume29en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchImage Processingen_US
dc.subject.fieldofresearchcode080106en_US
dc.titleGeneral Retinal Vessel Segmentation UsingRegularization-Based Multiconcavity Modelingen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_AU
gro.date.issued2010
gro.hasfulltextFull Text


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