Active Contouring with Morphology Assisted Automated Glaucoma Detection

Researchers are working day and night in current covid situation on human health studies where they have suspected a direct link of covid 19 with glaucoma. Also, glaucoma is considered as silent thief as it does not show symptoms at prestages in most cases and eventually with a few symptoms it pushes a person towards total blindness. So, with an early diagnosis, it can be treated somehow. When comes in detection, Computer Aided Design system with some morphological operations has become very popular for its' efficiency. In this work a Cup-to-Disc ratio based detection system is proposed where active contouring is used to detect optic cup and disc boundaries with some usage of filters for edge sharpening. Some morphological operations have also been used for boundary smoothening. However 94.2% accuracy is achieved working with a dataset of total 119 retinal fundus images.