title: Automated diagnosis of referable maculopathy in diabetic retinopathy screening.
abstract: This paper introduces an algorithm for the automated diagnosis of referable maculopathy in retinal images for diabetic retinopathy screening. Referable maculopathy is a potentially sight-threatening condition requiring immediate referral to an ophthalmologist from the screening service, and therefore accurate referral is extremely important. The algorithm uses a pipeline of detection and filtering of "peak points" with strong local contrast, segmentation of candidate lesions, extraction of features and classification by a multilayer perceptron. The optic nerve head and fovea are detected, so that the macula region can be identified and scanned. The algorithm is assessed against a reference standard database drawn from the Birmingham City Hospital (UK) diabetic retinopathy screening programme, against two possible modes of use: independent screening, and pre-filtering to reduce human screener workload.
authors.affiliation: University of Lincoln, UK. ahunter@lincoln.ac.uk
authors.forename: Andrew
authors.initials: A
authors.lastname: Hunter
authors.forename: James A
authors.initials: JA
authors.lastname: Lowell
authors.forename: Bob
authors.initials: B
authors.lastname: Ryder
authors.forename: Ansu
authors.initials: A
authors.lastname: Basu
authors.forename: David
authors.initials: D
authors.lastname: Steel
mesh_terms: D000465:Algorithms; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D006801:Humans; D006113:United Kingdom