Detection of optic disc and cup from color retinal images for automated diagnosis of glaucoma

Glaucoma is the major cause of ocular damage and vision loss in which increased Intraocular Pressure (IOP) of the eye progressively damages the optic nerve. In this proposed study, an automatic system is developed for glaucoma detection by extracting various features like vertical Cup to Disc Ratio (CDR), Horizontal to Vertical CDR (H-V CDR), Cup to Disc Area Ratio(CDAR), and Rim to Disc Area Ratio (RDAR) from digital fundus images through segmentation of Optic Disc (OD), cup and neuroretinal rim. OD is segmented using Geodesic active contour model and cup is detected using color information of the pallor region in M channel of CMY color space. The performance evaluation of the proposed technique has been carried out on 150 images comprising 75 normal and 75 glaucoma images using a set of supervised classifiers namely Naive Bayes(NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN). On the private database, the proposed system yields the highest accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV), specificity and sensitivity of 99.22%, 84.41%, 86.30%, 84% and 86.66% respectively using k-NN classifier. The results obtained by proposed technique indicate that this glaucoma detection system is beneficial for the clinicians in glaucoma screening programs.