Globally, glaucoma is the major cause of visual impairment. It is a chronic eye disease in which optic nerve damages progressively due to the raised Intraocular Pressure (IOP) of the eye. The technique proposed in this paper allows automatic detection of the glaucoma using digital fundus image by extracting the features like vertical cup to disc ratio (CDR), its area ratio, horizontal to vertical CDR and the ratio of area of blood vessels in inferior-superior region to that in the nasal-temporal region of the Optic Disc (OD) (ISNT Ratio) by segmenting OD, cup and blood vessels. The method proposed for OD segmentation is based on geodesic active contour model. The cup segmentation method is based on the structural properties and color information of the cup region and blood vessels are segmented by morphological technique. The performance evaluation of the proposed technique has been carried out on 200 images comprising 100 normal and 100 glaucomatous images. Support Vector Machine (SVM) is used to classify the fundus images into normal and glaucoma class with sensitivity, specificity, accuracy, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of 95%, 97%, 96%, 96. 94% and 95. 10% respectively. The results obtained by proposed technique indicate that the features are clinically important in glaucoma detection.

1. Pizzarello, et al. VISION 2020: The Right to Sight: a global initiative to eliminate avoidable blindness. Archives of ophthalmology. 2004, 615-620.
2. Cheng, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans. on Medical Imaging. 2013, 1019-1032.
3. Li, H. , and Chutatape, O. Automatic location of optic disc in retinal images. IEEE Intl. Conf. on Image Processing. 2001, 837–840.
4. Sinthanayothin, C. , Boyce, J. F. , Cook, H. L. and Williamson, T. H. Automated localization of the optic disc, fovea, and retinal blood vessels from digital color fundus images. British Journal of Ophthalmology. 1999, 902–910.
5. Joshi, G. , Sivaswamy, J. and Krishnadas, S. R. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans. On Medical Imaging. (Jun. 2011), 1192-1205.
6. Spaeth, G. L. , et al. Disc damage likelihood scale: reproducibility of a new method of estimating the amount of optic nerve damage caused by glaucoma. Trans. of American Ophthalmological Society. Soc. 2002, 181–186.
7. Muramatsu, C. , Hatanaka, Y. , Sawada, A. , Yamamoto, T. and Fujita, H. Computerized detection of Peripapillary chorioretinal atrophy by texture analysis. Engineering in Medicine and Biology Society, EMBC, 2011 Annual Intl. Conf. of the IEEE. 2011, 5947-5950.
8. Nayak, J. , Acharya, R. , Bhat, P. , Shetty, N. and Lim, T. -C. Automated diagnosis of glaucoma using digital fundus images. Journal of medical systems. 2009, 337–346.
9. Aquino, A. , Gegundez-Arias, M. E. and Marin, D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans. on Medical Imaging. (Nov. 2010), 1860-1869.
10. Hoover, A. and Goldbaum, M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. on Medical Imaging. (Aug. 2003) 951–958.
11. Ying, H. , Zhang, M. , Liu, J. -C. Fractal-based automatic localization and segmentation of optic disc in retinal images. Engineering in Medicine and Biology Society, EMBC 2007. 29th Annual Intl. Conf. of the IEEE. (Aug. 2007), 4139–4141.
12. Lalonde, M. , Beaulieu, M. and Gagnon, L. Fast and robust optic disk detection using pyramidal decomposition and Hausdorff-based template matching," IEEE Trans. on Medical Imaging. (Nov. 2001), 1193–1200.
13. Pallawala, P. M. D. S. , Hsu, W. , Lee, M. and Eong, K. A. Automated optic disc localization and contour detection using ellipse fitting and wavelet transform. Computer Vision-ECCV, Springer Berlin Heidelberg. 2004, 139-151.
14. Li, H. and Chutatape, O. Boundary detection of optic disk by a modified ASM method. Pattern Recognition. (Sep. 2003), 2093-2104.
15. Wong, D. W. K. , et al. Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI. Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual Intl. Conf. of the IEEE. 2008, 2266-2269.
16. Joshi, G. D. , Sivaswamy, J. , Karan, K. and Krishnadas, R. Optic disk and cup boundary detection using regional information. IEEE Intl. Symp. On Biomedical Imaging: From Nano to Macro. 2010, 948–951.
17. Wong, D. W. K. , Liu, J. , Lim, J. H. , Li, H. and Wong, T. Y. Automated detection of kinks from blood vessels for optic cup segmentation in retinal images. SPIE, Medical Imaging. Intl. Society for Optics and Photonics. (Mar. 2009).
18. Gonzalez, R. C. and Woods, R. E. Digital Image Processing, 2nd ed. , Prentice Hall, New Jersey, 2001.
19. Armaly, M. Genetic determination of cup/disc ratio of the optic nerve. Archives of ophthalmology. 1967, 35-43.
20. Bhartiya, Shibal, Gadia, R, Sethi, H. S. , and Panda, A. Clinical Evaluation of Optic Nerve Head in Glaucoma. Journal of Current Glaucoma Practice. 2010, 115-132.
21. Noronha, K. P. , Acharya, R. U. , Nayak, P. K. , Martis, R. J. and Bhandary, S. V. Automated classification of glaucoma stages using higher order cumulant features. Biomedical Signal Processing and Control. (Mar. 2014) 174-183.
22. Duda, R. O. and Hart, P. E. Use of the hough transformation to detect lines and curves in pictures. Communications of the ACM. 1972, 11–15.
23. Otsu, N. A threshold selection method from gray-scale histogram. IEEE Trans. on Syst. , Man. Cybern. 1978, 62–66.
24. Yin, F. , et al. Model-based optic nerve head segmentation on retinal fundus images. Engineering in Medicine and Biology Society, EMBC 2011. Annual Intl. Conf. of the IEEE. 2011, 2626-2629.
25. Caselles, V. , Kimmel, R. , and Sapiro, G. Geodesic active contours. Intl. journal of computer vision. 1997, 61-79.
26. Vapnik, V. The Nature of Statistical Learning Theory, Springer-Verlag, New York. 1995.

Fundus Image Pallor Glaucoma.