Optic Cup segmentation from retinal fundus images using Glowworm Swarm Optimization for glaucoma detection

Glaucoma is one of the diseases that damages the optic nerve of the eye and can result in permanent vision loss. Hence, it becomes essential to detect the disorder at an early stage. Optic cup segmentation from retinal fundus images is an important step for automated glaucoma diagnosis. In this paper, we have presented Glowworm Swarm Optimization algorithm that helps in automated detection of optic cup from retinal fundus images. The glowworms as agents help in the construction of the solutions by making use of the intensity gradient inside the cup region. The exploration capability of glowworms is derived from the adaptive neighbourhood behaviour, thereby making them capable of detecting optic cup region accurately, even in images having weak cup boundaries or low contrast. The proposed algorithm has been implemented and evaluated on RIM-ONE, DRIVE, STARE, DRIONS-DB and DIARETDB1 datasets for qualitative and quantitative analysis. The average overlapping error obtained is 22.1% for DRIONS-DB Database which is minimum as compared to other approaches namely thresholding based, Ellipse fitting and Ant Colony optimization algorithm. (C) 2020 Elsevier Ltd. All rights reserved.