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Low-Resolution Retinal Image Vessel Segmentation

2020-07Conference paper Open access
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datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
dc.contributor.authorZengin, Hasan
dc.contributor.authorCamara, José
dc.contributor.authorCoelho, Paulo
dc.contributor.authorRodrigues, João M. F.
dc.contributor.authorCunha, António
dc.date.accessioned2025-10-14T13:53:07Z
dc.date.available2025-10-14T13:53:07Z
dc.date.issued2020-07
dc.descriptionEISBN - 9783030491086
dc.descriptionConference name - 14th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020; Conference date - 19 July 2020 - 24 July 2020; Conference code - 242149
dc.description.abstractSegmentation process serves to aid the pathology diagnosing process since segmentation filters the interference from other anatomical structures and helps focus on the posterior segment structures of the eye, highlighting a set of signals that will serve for diagnosis of various retinal pathologies. Automatic retinal vessel segmentation can lead to a more accurate diagnosis. This paper presents a framework for automatic vessel segmentation of lower-resolution retinal images taken with a smartphone equipped with D-EYE lens. The framework is evaluated and the attained results were presented. A dataset was assembled and annotated of train models for automatic localisation retinal areas and for vessel segmentation. For the framework, two CNN based models were successfully trained, a Faster R-CNN that achieved a 96% correct detected of all regions with an MAE of 39 pixels, and a U-Net that achieved a DICE of 0.7547.eng
dc.description.sponsorshipThis project was financed by the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, through national funds, and co-funded by the FEDER, where applicable. This paper is financed by LARSyS - FCT Plurianual funding 2020–2023.
dc.identifier.citationZengin, H., Camara, J., Coelho, P., Rodrigues, J.M.F., Cunha, A. (2020). Low-Resolution Retinal Image Vessel Segmentation. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Applications and Practice. HCII 2020. Lecture Notes in Computer Science(), vol 12189. Springer, Cham. https://doi.org/10.1007/978-3-030-49108-6_44.
dc.identifier.doi10.1007/978-3-030-49108-6_44
dc.identifier.eissn1611-3349
dc.identifier.isbn9783030491079
dc.identifier.isbn9783030491086
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10400.8/14256
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-030-49108-6_44
dc.relation.ispartofLecture Notes in Computer Science
dc.relation.ispartofUniversal Access in Human-Computer Interaction. Applications and Practice
dc.rights.uriN/A
dc.subjectFaster R-CNN
dc.subjectU-Net
dc.subjectLow-resolution retinal images
dc.subjectSegmentation
dc.subjectScreening
dc.titleLow-Resolution Retinal Image Vessel Segmentationeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2020-07
oaire.citation.conferencePlaceCopenhagen, Denmark
oaire.citation.endPage627
oaire.citation.startPage611
oaire.citation.titleLecture Notes in Computer Science
oaire.citation.volume12189
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCoelho
person.givenNamePaulo
person.identifier2068530
person.identifier.ciencia-id3818-FA4F-CC36
person.identifier.orcid0000-0002-4383-0472
person.identifier.ridV-1924-2018
person.identifier.scopus-author-id57128835100
relation.isAuthorOfPublication0a2d9abe-a60d-4c77-a1b7-ad0755f025bc
relation.isAuthorOfPublication.latestForDiscovery0a2d9abe-a60d-4c77-a1b7-ad0755f025bc

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Segmentation process serves to aid the pathology diagnosing process since segmentation filters the interference from other anatomical structures and helps focus on the posterior segment structures of the eye, highlighting a set of signals that will serve for diagnosis of various retinal pathologies. Automatic retinal vessel segmentation can lead to a more accurate diagnosis. This paper presents a framework for automatic vessel segmentation of lower-resolution retinal images taken with a smartphone equipped with D-EYE lens. The framework is evaluated and the attained results were presented. A dataset was assembled and annotated of train models for automatic localisation retinal areas and for vessel segmentation. For the framework, two CNN based models were successfully trained, a Faster R-CNN that achieved a 96% correct detected of all regions with an MAE of 39 pixels, and a U-Net that achieved a DICE of 0.7547.
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