Automated Identification of Diabetic Retinopathy: A Survey

Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection