Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images

Abstract

Diabetic retinopathy (DR) is a microvascular complication of long-term diabetes and it is the major cause of visual impairment because of changes in blood vessels of the retina. Major vision loss because of DR is highly preventable with regular screening and timely intervention at the earlier stages. The presence of exudates is one of the primitive signs of DR and the detection of these exudates is the first step in automated screening for DR. Hence, exudates detection becomes a significant diagnostic task, in which digital retinal imaging plays a vital role. In this study, the authors propose an algorithm to detect the presence of exudates automatically and this helps the ophthalmologists in the diagnosis and follow-up of DR. Exudates are normally detected by their high grey-level variations and they have used an artificial neural network to perform this task by applying colour, size, shape and texture as the features. The performance of the authors algorithm has been prospectively tested by using DIARETDB1 database and evaluated by comparing the results with the ground-truth images annotated by expert ophthalmologists. They have obtained illustrative results of mean sensitivity 96.3%, mean specificity 99.8%, using lesion-based evaluation criterion and achieved a classification accuracy of 99.7%.

Correlation of duration, hypertension and glycemic control with microvascular complications of diabetes mellitus at a tertiary care hospital

“…It is estimated to affect up to 3 million people in the United States alone, by 2050 [17]. The main screening method is annual dilated eye examination to detect diabetic retinopathy [18,19]. Hyperglycaemia and hypertension are the main risk factors of developing retinopathy in patients with diabetes as also seen with our study [20].…”

Segmentation of Exudates via Color-based K-means Clustering and Statistical-based Thresholding

“…Their Method successfully extracted exudates with an average sensitivity of 78.28% over the DIARETDB1 dataset. Franklin and Rajan (2014) preprocessed the fundus images in the LAB color space using CLAHE to enhance its contrast. After eliminating the optic disc, the bright pixels were detected and classified as either "exudates" or "non-exudates" based on their high grey-level variations via employing an artificial neural network that utilized features such as color, size, shape, edge strength and texture.…”

Exudate and Blood Vessel Feature Extraction in Diabetic Retinopathy Patients using Morphology Operation

“…An algorithm to detect the presence of exudates automatically was proposed [13]. Research in [14] presented a method for automated identification of exudate pathologies in retinopathy images based on computational intelligence techniques.…”