Retinal Disease Diagnosis through Computer Aided Fundus Image Analysis

Abstract

Diabetic retinopathy, an asymptomatic complication of diabetes, is one of the leading causes of blindness in the world. The early detection and diagnosis can reduce the occurrence of severe vision loss due to diabetic retinopathy. Therefore, the present research work was conducted to diagnose symptomless clinical stages of diabetic retinopathy, i.e., non-proliferative diabetic retinopathy and progressive diabetic retinopathy. The diagnostic confirmation of diabetic retinopathy depends on the reliable detection and classification of bright lesions namely: exudates and cotton wool spots, and dark lesions namely: microaneurysms and hemorrhages present in retinal fundus images. However, variability within the retinal images makes difficult to distinguish dark and bright lesions in the presence of anatomical structures (landmarks) like blood vessels and optic disk. Therefore, it is necessary to eliminate any spurious, unwanted and false regions due to anatomical structures before the segmentation of retinal lesions. In addition, to design an efficient computer-aided diagnostic method a benchmark composite database, having varying attributes such as position, dimensions, shapes and color is required. Keeping all these facts in mind, a composite database has been designed in this work to provide an efficient and generalized computer-aided solution for the diagnosis of diabetic retinopathy In this research work, a composite database formed, includes 5048 retinal fundus images from two diverse sources: one is clinical source including 2942 images and other is six online available benchmark sources including overall 2106 images. The clinical database was developed by acquiring database of 2942 retinal images with varying color, brightness and quality from 482 different patients: 280 men (mean age: 51 years) and 202 women (mean age: 44 years) with an age range of 25 to 83 years over the period of January 2014 to July 2017.