A Multi-Step System for Screening and Localization of Hard Exudates in Retinal Images

The number of people being affected by Diabetes mellitus worldwide is increasing at an alarming rate. Monitoring of the diabetic condition and its effects on the human body are therefore of great importance. Of particular interest is diabetic retinopathy (DR) which is a result of prolonged, unchecked diabetes and affects the visual system. DR is a leading cause of blindness throughout the world. At any point of time 25-44% of people with diabetes are afflicted by DR. Automation of the screening and monitoring process for DR is therefore essential for efficient utilization of healthcare resources and optimizing treatment of the affected individuals. Such automation would use retinal images and detect the presence of specific artifacts such as hard exudates, hemorrhages and soft exudates (that may appear in the image) to gauge the severity of DR. In this paper, we focus on the detection of hard exudates. We propose a two step system that consists of a screening step that classifies retinal images as normal or abnormal based on the presence of hard exudates and a detection stage that localizes these artifacts in an abnormal retinal image. The proposed screening step automatically detects the presence of hard exudates with a high sensitivity and positive predictive value (PPV). The detection/localization step uses a k-means based clustering approach to localize hard exudates in the retinal image. Suitable feature vectors are chosen based on their ability to isolate hard exudates while minimizing false detections. The algorithm was tested on a benchmark dataset (DIARETDB1) and was seen to provide a superior performance compared to existing methods. The two-step process described in this paper can be embedded in a tele-ophthalmology system to aid with speedy detection and diagnosis of the severity of DR.