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

EARLY DIAGNOSIS OF DIABETIC RETINOPATHY BY THE DETECTION OF MICROANEURYSMS IN FUNDUS IMAGES

The detection of microaneurysms is crucial, as it is an early indicator of a complication of prolonged diabetes called Diabetic Retinopathy. In this paper, an automated approach is proposed to detect microaneurysms from retinal fundus images. Firstly, the magenta plane of the input image is extracted and a few preprocessing techniques are carried out. This is followed by the localization and the removal of the optic disk. The threshold value is determined and is optimized using Firefly algorithm. Then top hat transform is applied to detect the microaneurysms. The image quality parameters and the performance parameters were calculated and analyzed on the images of the DIARETDB1 database. The experimental results yielded a sensitivity of 99.80% before optimization and 100% after optimization

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm