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.