Binary Mask Pattern Segmentation in glaucoma detection - Arulmary, M; Victor, S.P (International Journal of Computer Sciences and Engineering, 2018)
Glaucoma detection is one of the most recent researches in medical field. There are several researches which mainly focus on optic cup to disc ratio to efficiently identify glaucoma. The objective of this paper is to identify glaucoma by creating a binary mask for optic cup and disc of glaucomatous eyes. The query image is segmented using these masks and identified as either normal or glaucomatous eyes. The proposed method is tested on RIM-ONE r3 database. The experimental results substantially proved that the proposed method achieved 95.29% specificity at 94.59% sensitivity with AUC of 0.869. The proposed method is also compared with existing methods and proved to work better than them.