title: Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs.
abstract: The optic disc is an important feature in the retina. We propose a method for the detection of the optic disc based on a supervised learning scheme. The method employs pixel and local neighbourhood features extracted from the ROI of a digital retinal fundus photograph. A support vector machine based classification mechanism is used to classify each image point as belonging to the cup and retina. The proposed method is evaluated on a sample image set of 68 retinal fundus images. The results show a high correlation (r>0.9) with the ground truth segmentation, with an overlap error of 6.02%, and found to be comparable to the inter-observer variability based on an independent second observer segmentation of the same data set.
authors.affiliation: Institute for Infocomm Research, A*STAR, Singapore. wkwong@i2r.a-star.edu.sg
authors.forename: D K
authors.initials: DK
authors.lastname: Wong
authors.forename: J
authors.initials: J
authors.lastname: Liu
authors.forename: N M
authors.initials: NM
authors.lastname: Tan
authors.forename: F
authors.initials: F
authors.lastname: Yin
authors.forename: B H
authors.initials: BH
authors.lastname: Lee
authors.forename: T Y
authors.initials: TY
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D001331:Automation; D005654:Fundus Oculi; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007858:Learning; D009898:Optic Disk / Q000033:anatomy & histology*; D010781:Photography / Q000379:methods*