Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/5636
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dc.contributor.authorMaria Inês Meyeren
dc.contributor.authorCosta,Pedroen
dc.contributor.authorAdrian Galdranen
dc.contributor.authorAna Maria Mendonçaen
dc.contributor.authorAurélio Campilhoen
dc.date.accessioned2018-01-06T13:41:34Z-
dc.date.available2018-01-06T13:41:34Z-
dc.date.issued2017en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/5636-
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-59876-5_56en
dc.description.abstractRetinal vessel segmentation is a fundamental and well-studied problem in the retinal image analysis field. The standard images in this context are color photographs acquired with standard fundus cameras. Several vessel segmentation techniques have been proposed in the literature that perform successfully on this class of images. However, for other retinal imaging modalities, blood vessel extraction has not been thoroughly explored. In this paper, we propose a vessel segmentation technique for Scanning Laser Opthalmoscopy (SLO) retinal images. Our method adapts a Deep Neural Network (DNN) architecture initially devised for segmentation of biological images (U-Net), to perform the task of vessel segmentation. The model was trained on a recent public dataset of SLO images. Results show that our approach efficiently segments the vessel network, achieving a performance that outperforms the current state-of-the-art on this particular class of images. © Springer International Publishing AG 2017.en
dc.languageengen
dc.relation6835en
dc.relation6071en
dc.relation6381en
dc.relation6825en
dc.rightsinfo:eu-repo/semantics/embargoedAccessen
dc.titleA Deep Neural Network for Vessel Segmentation of Scanning Laser Ophthalmoscopy Imagesen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:C-BER - Articles in International Conferences

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