{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:42:28Z","timestamp":1761198148034,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agencia Estatal de Investigaci\u00f3n, Spain","award":["PID2020-113919RB-I00"],"award-info":[{"award-number":["PID2020-113919RB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Glaucoma 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.<\/jats:p>","DOI":"10.3390\/diagnostics12123210","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T06:58:29Z","timestamp":1671433109000},"page":"3210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Superpixel-Based Optic Nerve Head Segmentation Method of Fundus Images for Glaucoma Assessment"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9068-7728","authenticated-orcid":false,"given":"Francisco J.","family":"\u00c1vila","sequence":"first","affiliation":[{"name":"Departamento de F\u00edsica Aplicada, Universidad de Zaragoza, 50009 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9711-8034","authenticated-orcid":false,"given":"Juan M.","family":"Bueno","sequence":"additional","affiliation":[{"name":"Laboratorio de \u00d3ptica, Centro de Investigaci\u00f3n en \u00d3ptica y Nanof\u00edsica, Universidad de Murcia, 30100 Murcia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Rem\u00f3n","sequence":"additional","affiliation":[{"name":"Departamento de F\u00edsica Aplicada, Universidad de Zaragoza, 50009 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1167\/tvst.4.2.1","article-title":"Why do people (still) go blind from Glaucoma?","volume":"4","author":"Remo","year":"2015","journal-title":"Trans. Vis. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1136\/bjophthalmol-2013-304326","article-title":"OCT for glaucoma diagnosis, screening and detection of glaucoma progression","volume":"98","author":"Bussel","year":"2013","journal-title":"Br. J. Ophthalmol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1097\/ICU.0b013e3282f4b1c2","article-title":"New developments in scanning laser polarimetry for glaucoma","volume":"19","author":"Lemij","year":"2008","journal-title":"Curr. Opin. Opthalmol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1097\/APO.0000000000000085","article-title":"Confocal scan laser ophthalmoscope for diagnosing glaucoma: A systematic review and meta-analysis","volume":"4","author":"Yaghoubi","year":"2015","journal-title":"Asia Pac. J. Ophthalmol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1097\/IJG.0000000000000354","article-title":"Automated Detection of Glaucoma from Topographic Features of the Optic Nerve Head in Color Fundus Photographs","volume":"25","author":"Chakrabarti","year":"2016","journal-title":"J. Glaucoma"},{"unstructured":"Walter, T., and Klein, J.C. Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques. Proceedings of the Second International Symposium on Medical Data Analysis.","key":"ref_6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s10916-017-0859-4","article-title":"A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis","volume":"42","author":"Haleem","year":"2018","journal-title":"J. Med. Syst."},{"unstructured":"Zhu, X., and Rangayyan, R.M. (2008, January 20\u201325). Detection of the optic disc in images of the retina using the hough transform. Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada.","key":"ref_8"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TMI.2010.2053042","article-title":"Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques","volume":"29","author":"Aquino","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"An active contour model without edges","volume":"10","author":"Chan","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","first-page":"2144","article-title":"Segmentation of optic nerve head rim in color fundus photographs by probability based active shape model","volume":"53","author":"Tang","year":"2012","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2745183","DOI":"10.1155\/2019\/2745183","article-title":"Automatic optic disc segmentation based on modified local image fitting model with shape prior information","volume":"2019","author":"Gao","year":"2019","journal-title":"J. Healthc. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1016\/j.patcog.2006.10.015","article-title":"Optic disk feature extraction via modified deformable model technique for glaucoma analysis","volume":"40","author":"Xu","year":"2007","journal-title":"Pattern Recognit."},{"doi-asserted-by":"crossref","unstructured":"Ayub, J., Ahmad, J., Muhammad, J., Aziz, L., Ayub, S., Akram, U., and Basit, I. (2016, January 11\u201312). Glaucoma detection through optic disc and cup segmentation using K-mean clustering. Proceedings of the 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, Pakistan.","key":"ref_14","DOI":"10.1109\/ICECUBE.2016.7495212"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1109\/TCYB.2015.2409119","article-title":"A level set approach to image segmentation with intensity inhomogeneity","volume":"46","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Cybern."},{"unstructured":"Lu, L., Wang, X., Carneiro, G., and Yang, L. (2019). Glaucoma Detection Based on Deep Learning Network in Fundus Image. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, Springer. Advances in Computer Vision and Pattern Recognition.","key":"ref_16"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5025","DOI":"10.1038\/s41598-020-62022-x","article-title":"Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures","volume":"10","author":"Park","year":"2020","journal-title":"Sci. Rep."},{"doi-asserted-by":"crossref","unstructured":"Sreng, S., Maneerat, N., Hamamoto, K., and Win, K. (2020). Deep Learning for Optic Disc Segmentation andGlaucoma Diagnosis on Retinal Images. Appl. Sci., 10.","key":"ref_18","DOI":"10.3390\/app10144916"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1038\/s41598-021-81554-4","article-title":"A combined convolutional and recurrent neural network for enhanced glaucoma detection","volume":"11","author":"Gheisari","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101454","DOI":"10.1016\/j.bspc.2019.01.003","article-title":"An automated glaucoma screening system using cup-to-disc ratio via Simple Linear Iterative Clustering superpixel approach","volume":"53","author":"Mohamed","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.artmed.2008.04.005","article-title":"Identification of the optic nerve head with genetic algorithms","volume":"43","author":"Carmona","year":"2008","journal-title":"Artif. Intell. Med."},{"key":"ref_22","first-page":"3065","article-title":"ORIGA(-light): An online retinal fundus image database for glaucoma analysis and research","volume":"2010","author":"Zhang","year":"2010","journal-title":"Annu. Int. Conf. IEEE Engin. Med. Biol. Soc."},{"doi-asserted-by":"crossref","unstructured":"Ren, X., and Malik, J. (2003, January 13\u201316). Learning a classification model for segmentation. Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, France.","key":"ref_23","DOI":"10.1109\/ICCV.2003.1238308"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1097\/IJG.0000000000000988","article-title":"Deep-learning Classifier with an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity","volume":"27","author":"Masumoto","year":"2018","journal-title":"J. Glaucoma"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/s41433-020-01191-5","article-title":"Deep learning in glaucoma with optical coherence tomography: A review","volume":"35","author":"Ran","year":"2021","journal-title":"Eye"},{"key":"ref_26","first-page":"2626","article-title":"Model-based optic nerve head segmentation on retinal fundus images","volume":"2011","author":"Yin","year":"2011","journal-title":"IEEE Int. Conf. Eng. Med. Biol. Soc."},{"key":"ref_27","first-page":"5777","article-title":"Intelligent fusing of cup-to-disc ratio determination methods for glaucoma diagnosis","volume":"2009","author":"Wong","year":"2009","journal-title":"Int. Conf. Engin. Med. Biol. Soc."},{"key":"ref_28","first-page":"1","article-title":"Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis","volume":"14","author":"Xu","year":"2011","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.compmedimag.2014.10.002","article-title":"Robust multi-scale superpixel classification for optic cup localization","volume":"40","author":"Tan","year":"2015","journal-title":"Comput. Med. Imaging Graph"},{"key":"ref_30","first-page":"58","article-title":"Efficient Optic Cup Detection from Intra-image Learning with Retinal Structure Prior","volume":"Volume 7510","author":"Ayache","year":"2012","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2012"},{"key":"ref_31","first-page":"788","article-title":"Optic cup segmentation for glaucoma detection using low-rank superpixel representation","volume":"17","author":"Xu","year":"2014","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/TMI.2013.2247770","article-title":"Superpixel classification based optic disc and optic cup segmentation for glaucoma screening","volume":"32","author":"Cheng","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_33","first-page":"OP157","article-title":"Vertical elongation of the optic cup in glaucoma","volume":"77","author":"Weismann","year":"1973","journal-title":"Trans. Am. Acad. Ophthalmol. Otolaryngol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.ophtha.2020.01.023","article-title":"Longitudinal macular structure-function relationship in glaucoma","volume":"127","author":"Mohammadzadeh","year":"2020","journal-title":"Ophtalmology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4378","DOI":"10.1167\/iovs.14-14227","article-title":"Optic Nerve Head and Peripapillary Morphometrics in Myopic Glaucoma","volume":"55","author":"Lee","year":"2014","journal-title":"Glaucoma. Investig. Opthalmol. Vis. Sci."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/12\/3210\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:23Z","timestamp":1760147003000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/12\/3210"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,17]]},"references-count":35,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["diagnostics12123210"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics12123210","relation":{},"ISSN":["2075-4418"],"issn-type":[{"type":"electronic","value":"2075-4418"}],"subject":[],"published":{"date-parts":[[2022,12,17]]}}}