Published on September 2020 | Image processing

Segmentation of Blood vessels in Retinal Fundus Images for Early Detection of Retinal Disorders

Retina forms an essential part of human eye and statistics indicate that significant global population is affected by retinal disorders due to non detection at early symptoms. Most of retinal disorders are progressive in nature and remain passive for years together without causing any visual indication of disorder even to the subject themselves. The disorders do not cause any immediate loss of vision. It could be noted that the apparent migration from perfect to significant loss of vision in the patient is very marginal and thin that even an ophthalmologist would find it hard to detect it in the first case. Hence automates and intelligent methods of analysis of retinal scanned images are quite necessary to improve accuracy and detection time to aid in early detection and consequent treatment. This paper presents the findings of a vast literature survey done with respect to automated detection techniques by analyzing their underlying principles and obtained performance results. The entire survey has been done based on two main evaluation metrics namely detection accuracy and time of convergence. This is based on the underlying principle that migration from manual and conventional methods to automated systems is to improve the accuracy by overcoming the errors incurred in manual detection methods and at the same time to reduce the painstakingly long time required in the manual method of observation and detection. This paper has been systematically organized and presented into sections with emphasis on segmentation which is the major contributor in the detection process. An accurate detection is reflective of a precise and efficient segmentation algorithm