Automated fundus image analysis for screening Diabetic Retinopathy
Overview
Diabetic retinopathy is the leading cause of blindness in people of working age in the developed world. The blindness due to diabetes costs US government and general public $500 million annually. A WHO collaborative study projected that the global diabetic burden is expected to increase to 221 million people by 2010. However treatment can prevent visual loss from sight-threatening retinopathy if detected early. In order to address the impact of diabetes, screening schemes are currently being put into place based on digital fundal photography. However, there are concerns regarding the cost of any screening scheme used for detecting sight-threatening diabetic retinopathy in the population. One of the greatest sources of expenditure in setting up any diabetic retinopathy screening program is the cost of financing trained manual graders. If automated detection programs are able to exclude a large number of those patients who have no diabetic retinopathy, it will reduce the workload of the trained graders and this reduce costs.
Our current research interest is focused on automated diagnosis of diabetic retinopathy using digital fundus images. The images will be graded onto diabetic retinopathy scale (10 - 80 ) based on the type, quantity and the area of lesions present in the eye. Feature extraction is the first step in the classification of these images. The challenge lies in extracting robust features as the image color varies from patient to patient.Back