Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/22313
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dc.contributor.authorCamara, Joséen_US
dc.contributor.authorNeto, Alexandreen_US
dc.contributor.authorPires, Ivan Miguelen_US
dc.contributor.authorVillasana, María Vanessaen_US
dc.contributor.authorZdravevski, Eftimen_US
dc.contributor.authorCunha, Antónioen_US
dc.date.accessioned2022-08-16T08:11:48Z-
dc.date.available2022-08-16T08:11:48Z-
dc.date.issued2022-04-08-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/22313-
dc.description.abstractGlaucoma is a chronic optic neuropathy characterized by irreversible damage to the retinal nerve fiber layer (RNFL), resulting in changes in the visual field (VC). Glaucoma screening is performed through a complete ophthalmological examination, using images of the optic papilla obtained in vivo for the evaluation of glaucomatous characteristics, eye pressure, and visual field. Identifying the glaucomatous papilla is quite important, as optical papillary images are considered the gold standard for tracking. Therefore, this article presents a review of the diagnostic methods used to identify the glaucomatous papilla through technology over the last five years. Based on the analyzed works, the current state-of-the-art methods are identified, the current challenges are analyzed, and the shortcomings of these methods are investigated, especially from the point of view of automation and independence in performing these measurements. Finally, the topics for future work and the challenges that need to be solved are proposed.en_US
dc.publisherMDPIen_US
dc.relation.ispartofDiagnosticsen_US
dc.subjectglaucoma; image processing; glaucomatous papilla; clinical data; disease tracking; eyeen_US
dc.titleA Comprehensive Review of Methods and Equipment for Aiding Automatic Glaucoma Trackingen_US
dc.typeArticleen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
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