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dc.contributor.authorLam, Benson SY
dc.contributor.authorGao, Yongsheng
dc.contributor.authorLiew, Alan Wee-Chung
dc.date.accessioned2017-05-03T13:11:13Z
dc.date.available2017-05-03T13:11:13Z
dc.date.issued2010
dc.date.modified2011-02-09T06:41:48Z
dc.identifier.issn0278-0062
dc.identifier.doi10.1109/TMI.2010.2043259
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent525887 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherI E E E
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom1369
dc.relation.ispartofpageto1381
dc.relation.ispartofissue7
dc.relation.ispartofjournalI E E E Transactions on Medical Imaging
dc.relation.ispartofvolume29
dc.rights.retentionY
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchImage processing
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode46
dc.subject.fieldofresearchcode460306
dc.subject.fieldofresearchcode40
dc.titleGeneral Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 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.
gro.date.issued2010
gro.hasfulltextFull Text
gro.griffith.authorGao, Yongsheng
gro.griffith.authorLiew, Alan Wee-Chung


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