Publication:
Superpixel-Based Optic Nerve Head Segmentation Method of Fundus Images for Glaucoma Assessment

Simple item page
dc.contributor.authorÁvila, Francisco J.
dc.contributor.authorBueno García, Juan Manuel
dc.contributor.authorRemón, Laura
dc.contributor.departmentSin departamento asociado
dc.contributor.otherCentro de Investigación en Óptica y Nanofísica (CIOyN)
dc.date.accessioned2025-12-26T09:46:00Z
dc.date.available2025-12-26T09:46:00Z
dc.date.copyright© 2022 by the authors
dc.date.issued2022-12-17
dc.description.abstractGlaucoma disease is the second leading cause of blindness in the world. This progressive ocular neuropathy is mainly caused by uncontrolled high intraocular pressure. Although there is still no cure, early detection and appropriate treatment can stop the disease progression to low vision and blindness. In the clinical practice, the gold standard used by ophthalmologists for glaucoma diagnosis is fundus retinal imaging, in particular optic nerve head (ONH) subjective/manual examination. In this work, we propose an unsupervised superpixel-based method for the optic nerve head (ONH) segmentation. An automatic algorithm based on linear iterative clustering is used to compute an ellipse fitting for the automatic detection of the ONH contour. The tool has been tested using a public retinal fundus images dataset with medical expert ground truths of the ONH contour and validated with a classified (control vs. glaucoma eyes) database. Results showed that the automatic segmentation method provides similar results in ellipse fitting of the ONH that those obtained from the ground truth experts within the statistical range of inter-observation variability. Our method is a user-friendly available program that provides fast and reliable results for clinicians working on glaucoma screening using retinal fundus images.
dc.formatapplication/pdf
dc.format.extent9
dc.identifier.citationDiagnostics 2022, 12, 3210.
dc.identifier.doihttps://doi.org/10.3390/diagnostics12123210
dc.identifier.eissn2075-4418
dc.identifier.urihttp://hdl.handle.net/10201/182649
dc.languageeng
dc.publisherMDPI
dc.relationThis work has been partially supported by the Agencia Estatal de Investigación, Spain (grant PID2020-113919RB-I00).
dc.relation.publisherversionhttps://www.mdpi.com/2075-4418/12/12/3210
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectArtificial intelligence
dc.subjectSuperpixel segmentation
dc.subjectGlaucoma
dc.subject.odsObjetivo 3: Salud
dc.titleSuperpixel-Based Optic Nerve Head Segmentation Method of Fundus Images for Glaucoma Assessment
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication460f1110-b358-474a-9e08-f92fabe4be2b
relation.isAuthorOfPublication.latestForDiscovery460f1110-b358-474a-9e08-f92fabe4be2b
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2022_DIAGNOSTICS_Superpixel&Glaucoma.pdf
Size:
2.9 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.37 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Artículos