Image Resizing Impact on Optic Disc and Optic Cup Segmentation

dc.contributor.authorVirbukaite, Sandra
dc.contributor.authorBernataviciene, Jolita
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-02T10:34:14Z
dc.date.available2022-09-02T10:34:14Z
dc.date.issued2022
dc.description.abstract-translatedOptic disc (OD) and Optic Cup (OC) segmentation play an important role in the automatic assessment of eye health where the Convolutional Neural Networks (CNNs) have been extensively employed. The application of CNNs requires identical image size to work properly but the eye fundus images vary due to different datasets. In this paper we evaluate eye fundus image resizing level impact on OD and OC segmentation. For this evaluation we apply the most popular medical images segmentation autoencoder named U-Net. The experiments demonstrate that OD and OC segmentation results are improved averagely by 5.5 percent resizing images to size of 512x512 than 128x128.en
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 306-309.en
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.39
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49610
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subjectsegmentace optického diskucs
dc.subjectsegmentace optického pohárucs
dc.subjectkonvoluční neuronové sítěcs
dc.subject.translatedoptic disc segmentationen
dc.subject.translatedoptic cup segmentationen
dc.subject.translatedconvolutional neural networksen
dc.titleImage Resizing Impact on Optic Disc and Optic Cup Segmentationen
dc.typeconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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