ABSTRACT:
This paper aims to find an efficient hybrid method to diagnose diabetic retinopathy, which is an anomaly in the human eyes that occur due to the decrease of insulin content in the blood. Damages to the blood vessels in the light-sensitive tissue of the eye is its root cause. The symptoms of diabetic retinopathy are hemorrhages, exudates and micro-aneurysms. Eventually it will lead to total blindness. This erratic disorder is developed in people having both type-1 and type-2 diabetes. The longer period of time you have uncontrolled blood sugar levels, it is more likely that this condition of diabetic retinopathy may arise. Since the number of diabetic retinopathy patients are high in number, the significance of automating the diagnostic process is much more relevant. In order to diagnose this disease automatically, a hybrid and efficient system has been developed to interpret and analyse the 2D fundus images. Grayscale conversion and Contrast Level Adaptive Histogram Enhancement (CLAHE) has been performed as a pre-processing step to improve the quality of the input image which will further aid in blood vessel extraction and exudates determination in a better way. The pre-processed image is further manipulated with the Kirsch’s template for the blood vessel extraction. Subsequently, the features of the images are extracted from the morphologically processed images through a multi-level Maximally Stable Extremal Regions (MSER) to precisely extract and identify the exudates from the eye. The determination of exudates helps the ophthalmologist to diagnose the diabetic retinopathy and further proceed with respective treatment.