**4. Conclusions**

Literature survey reveals that previous attempts were made to detect Glaucoma by extracting predominantly CDR or wavelet-based features. In our research work of Glaucoma detection, we have enhanced the efficiency of Glaucoma detection system by processing and analyzing additional fundus image features apart from the CDR or wavelet features leading to a hybrid approach.

We have also improved the efficiency of CDR-based Glaucoma detection system by a template for correlation with input fundus images. As seen from Results section, template correlation approach is better than intensity threshold techniques. Additional features, RDR and neuroretinal rim thickness, have improved the efficiency of the system.

We have introduced vessel-based features for Glaucoma detection which is a significant step toward Glaucoma detection. As seen in the literature, reductions in vessel diameters are the indication of Glaucoma. To detect this we have incorporated database where these significant features can be stored and monitored for regular visits of patients.

Wavelet-based strategy has also been used by us to increase the efficiency of the system. We have used feedforward neural networks to classify the input fundus images using wavelet-based textures.
