title: Automatic screening of age-related macular degeneration and retinal abnormalities.
abstract: We describe a novel approach for screening retinal imagery to detect evidence of abnormalities. In this paper, we focus our efforts on age-related macular degeneration (AMD), a pathology that may often go undetected in the early or intermediate stages, and can lead to a neovascular form often resulting in blindness, if untreated. Our strategy for retinal anomaly detection is to employ a single class classifier applied to fundus imagery. We use a multiresolution locally-adaptive scheme that identifies both normal and anomalous regions within the retina. We do this by using a hybrid parametric/non-parametric characterization of the support of the probability distribution of normal retinal tissue in color and intensity feature space. We apply this approach to screen for evidence of AMD on a dataset of 66 healthy and pathological cases and found a detection sensitivity and specificity of 95% and 96%.
authors.affiliation: Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723, USA. philippe.burlina@jhuapl.edu
authors.forename: P
authors.initials: P
authors.lastname: Burlina
authors.forename: D E
authors.initials: DE
authors.lastname: Freund
authors.forename: B
authors.initials: B
authors.lastname: Dupas
authors.forename: N
authors.initials: N
authors.lastname: Bressler
mesh_terms: D000375:Aging / Q000473:pathology*; D000465:Algorithms; D001331:Automation; D006801:Humans; D008268:Macular Degeneration / Q000175:diagnosis*; D012164:Retinal Diseases / Q000175:diagnosis*