• Srinivasan C
  • Suneel Dubey
  • Ganeshbabu T R



Glaucoma, colour fundus image, GLCM, SVM classifier, Optical density image.


In this paper, Gray Level Co-occurrence Matrix (GLCM) features are effectively utilized for glaucoma diagnosis. Early diagnosis of glaucoma is important to protect vision loss. The proposed system uses four GLCM features such as Contrast, Correlation, Energy, and Homogeneity for the diagnosis. In order to effectively use these features for glaucoma detection they are extracted using the optical density transformed fundus image along with the original features. The classification of fundus image into normal or abnormal is obtained by the Support Vector Machine (SVM) classifier. An internal database of 200 images is utilized for the performance analysis. The results show that the proposed approach helps the ophthalmologists to make their decision very accurately. The proposed system provides 95% classification accuracy.


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Author Biographies

Srinivasan C

Research scholar, Department of CSE, Maharishi University of Information Technology, Lucknow, India

Suneel Dubey

Associate Professor, Department of CSE, Maharishi University of Information Technology, Lucknow, India.

Ganeshbabu T R

Professor, Department of ECE, Muthayammal Engineering College, Rasipuram, India


1. Gopal Datt Joshi, Jayanthi Sivaswamy & Krishnadas, S.R, “Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment”, IEEE Transactions On Medical Imaging, vol. 30, no. 6, pp. 1192-1205, JUNE 2011.

2. Anum Abdul Salam, Usman Akram, M., Sarmad Abbas & Syed M. Anwar, “Optic Disc Localization using Local Vessel Based Features and Support Vector Machine”, IEEE conference on Bioinformatics and Bioengineering, pp. 1-6, 2015.

3. Shishir Maheshwari, Ram Bilas Pachori & Rajendra Acharya, U., “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted from Fundus Image”, IEEE Journal of Biomedical and Health Informatics, 2016.

4. Simonthomas, S., Thulasi, N. & Asharaf, P., “Automated Diagnosis of Glaucoma using Haralick Texture Features”, IEEE conference on Information Communication and Embedded Systems, pp. 1-6, 2014.

5. Gaurav O. Gajbhiye, Ashok N. Kamthane, “Automatic Classification of Glaucomatous Images using Wavelet and Moment Feature”, IEEE conference on INDICON, pp. 1-5, 2015.

6. Harshvardhan, G., Venkateswaran, N. & Padmapriya, N., “Assessment of Glaucoma with Ocular Thermal Images using GLCM Techniques and Logistic Regression classifier”, IEEE conference on Wireless Communications, Signal Processing and Networking ,2016.

7. Gayathri Devi, T.M., Sudha, S. & Suraj, P., “Glaucoma detection from retinal images”, IEEE 2nd International Conference on Electronics and Communication Systems, pp. 423- 428, 2015.

8. Gayathri, R., Dr. Rao, P.V. & Anma, S., “Automated Glaucoma Detection System based on Wavelet Energy features and ANN”, IEEE International Conference on Advances in Computing, Communications and Informatics, pp. 2808-2812, 2014.

9. Andres Diaz, et al, “Glaucoma Diagnosis by Means of Optic Cup Feature Analysis in Color Fundus Images”, IEEE Conference on Signal Processing, pp. 2055-2059, 2016.

10. Abhishek Dey & Samir K. Bandyopadhyay, “Automated Glaucoma Detection Using Support Vector Machine Classification Method”, British Journal of Medicine & Medical Research, vol. 11, no. 12, p. 1, 2016.

11. Haralick, R.M., “Statistical and Structural Approaches to Texture”, Proceedings of the IEEE, vol.67, no.5, pp. 786-804, 1979.