A machine-learning approach to retrieving diabetic retinopathy images

Abstract

Diabetic retinopathy (DR) is a vision-threatening complication that affects people suffering from diabetes. Diagnosis of DR during early stages can significantly reduce the risk of severe vision loss. The process of DR severity grading is prone to human error and it also depends on the expertise of the ophthalmologist. As a result, many researchers have started exploring automated detection and evaluation of diabetic retinal lesions. Unfortunately, to date there is no automated system that can perform DR lesion detection with the accuracy that is comparable to a human expert. In this poster, we present a novel way of employing content-based image retrieval for providing a clinician with instant reference to archival and standardized DR images that are used for assisting the ophthalmologist with the diagnosis of a given DR image. The focus of the poster is on retrieving DR images with two significant DR clinical findings, namely, microaneurysm (MA) and neovascularization (NV). We propose a multi-class multiple-instance DR image retrieval framework that makes use of a modified color correlogram (CC) and statistics of steerable Gaussian filter (SGF) responses. Experiments using real DR images with comparisons to other prior-art methods demonstrate the improved performance of the proposed approach.