**Acknowledgements**

We would like to express our sincere gratitude to Ophthalmology department of KLE Society's Dr. Prabhakar Kore Hospital & M.R.C., Belagavi, India, for providing us with necessary guidance and resources required for experimentation and special thanks to High-Resolution Fundus (HRF) Image Database available on the Internet [13]. We would like to thank Research Center, E & C Department of Jawaharlal Nehru National College of Engineering, Shivamogga, India, and Electronics and Communication Department, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Belagavi, India, for technical guidance and resources, without which venture would not have been possible.

**115**

**Author details**

Nataraj Vijapur1

Karnataka, India

\* and R. Srinivasarao Kunte2

\*Address all correspondence to: nvijapur@gmail.com

provided the original work is properly cited.

1 KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Belagavi,

2 Sahyadri College of Engineering and Management, Mangalore, Karnataka, India

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Efficient Computer-Aided Techniques to Detect Glaucoma*

*DOI: http://dx.doi.org/10.5772/intechopen.89799*

*Efficient Computer-Aided Techniques to Detect Glaucoma DOI: http://dx.doi.org/10.5772/intechopen.89799*

*Visual Impairment and Blindness - What We Know and What We Have to Know*

**Results of feature extraction using NSK template**

**Results of using wavelets and neural classifier**

85% 94% 95% 98.57%

83% 90% 94% 94%

**Results of final integrated glaucoma classifier**

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

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

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

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

We would like to express our sincere gratitude to Ophthalmology department of KLE Society's Dr. Prabhakar Kore Hospital & M.R.C., Belagavi, India, for providing us with necessary guidance and resources required for experimentation and special thanks to High-Resolution Fundus (HRF) Image Database available on the Internet [13]. We would like to thank Research Center, E & C Department of Jawaharlal Nehru National College of Engineering, Shivamogga, India, and Electronics and Communication Department, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Belagavi, India, for technical guidance and resources, without which

the CDR or wavelet features leading to a hybrid approach.

**114**

**4. Conclusions**

*Results of glaucoma classifier.*

Glaucoma dataset

Normal dataset

**Table 1.**

**Image type Results of** 

**using vesselbased feature VRI**

efficiency of the system.

regular visits of patients.

**Acknowledgements**

images using wavelet-based textures.

venture would not have been possible.
