The use of datasets of bad quality images to define fundus image quality

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dc.contributor.authorMenolotto, Matteo
dc.contributor.authorGiardini, Mario E.
dc.contributor.funderRosetrees Trusten
dc.contributor.funderSight Research UKen
dc.date.accessioned2022-09-20T09:29:45Z
dc.date.available2022-09-20T09:29:45Z
dc.date.issued2022-09-08
dc.date.updated2022-09-02T15:12:45Z
dc.description.abstractScreening programs for sight-threatening diseases rely on the grading of a large number of digital retinal images. As automatic image grading technology evolves, there emerges a need to provide a rigorous definition of image quality with reference to the grading task. In this work, on two subsets of the CORD database of clinically gradable and matching non-gradable digital retinal images, a feature set based on statistical and on task-specific morphological features has been identified. A machine learning technique has then been demonstrated to classify the images as per their clinical gradeability, offering a proxy for a rigorous definition of image quality.en
dc.description.sponsorshipRosetrees Trust (Grant M720); Sight Research UK (Grant SAC030)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMenolotto, M. and Giardini, M. E. (2022) 'The use of datasets of bad quality images to define fundus image quality', 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Glasgow, United Kingdom, 11-15 July, pp. 504-507. doi: 10.1109/EMBC48229.2022.9871614en
dc.identifier.doi10.1109/EMBC48229.2022.9871614en
dc.identifier.eissn2694-0604
dc.identifier.endpage507en
dc.identifier.isbn978-1-7281-2783-5
dc.identifier.isbn978-1-7281-2782-8
dc.identifier.issn2375-7477
dc.identifier.startpage504en
dc.identifier.urihttps://hdl.handle.net/10468/13619
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.rights© 2022, the Authors. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectSight-threatening diseasesen
dc.subjectDigital retinal imagesen
dc.subjectImage qualityen
dc.titleThe use of datasets of bad quality images to define fundus image qualityen
dc.typeConference itemen
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