000121891 001__ 121891
000121891 005__ 20240319081029.0
000121891 0247_ $$2doi$$a10.3390/diagnostics12123210
000121891 0248_ $$2sideral$$a132242
000121891 037__ $$aART-2022-132242
000121891 041__ $$aeng
000121891 100__ $$0(orcid)0000-0002-9068-7728$$aÁvila, Francisco J.$$uUniversidad de Zaragoza
000121891 245__ $$aSuperpixel-Based Optic Nerve Head Segmentation Method of Fundus Images for Glaucoma Assessment
000121891 260__ $$c2022
000121891 5060_ $$aAccess copy available to the general public$$fUnrestricted
000121891 5203_ $$aGlaucoma 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.
000121891 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113919RB-I00
000121891 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000121891 590__ $$a3.6$$b2022
000121891 592__ $$a0.67$$b2022
000121891 591__ $$aMEDICINE, GENERAL & INTERNAL$$b64 / 169 = 0.379$$c2022$$dQ2$$eT2
000121891 593__ $$aClinical Biochemistry$$c2022$$dQ2
000121891 594__ $$a3.6$$b2022
000121891 655_4 $$ainfo:eu-repo/semantics/conferenceObject$$vinfo:eu-repo/semantics/publishedVersion
000121891 700__ $$aBueno, Juan M.
000121891 700__ $$0(orcid)0000-0002-3979-4528$$aRemón, Laura$$uUniversidad de Zaragoza
000121891 7102_ $$12002$$2647$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Óptica
000121891 773__ $$g12, 12 (2022), 3210 [9 pp.]$$tDiagnostics$$x2075-4418
000121891 8564_ $$s3045742$$uhttps://zaguan.unizar.es/record/121891/files/texto_completo.pdf$$yVersión publicada
000121891 8564_ $$s2699280$$uhttps://zaguan.unizar.es/record/121891/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000121891 909CO $$ooai:zaguan.unizar.es:121891$$particulos$$pdriver
000121891 951__ $$a2024-03-18-17:02:48
000121891 980__ $$aARTICLE