• Ramesh C. Department of ECE, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamilnadu, India
  • Udayakumar E. Department of ECE, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamilnadu, India
  • Yogeshwaran K. Department of ECE, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamilnadu, India



Diabetic Retinopathy (DR), Glaucoma, Optic Disc (OD), Segmentation, Retinal image, Fovea


Objective: Image processing technique is utilized in the medical field widely nowadays. Hence, therefore, this technique is used to extract the different features like blood vessels, optic disk, macula, fovea etc. automatically of the retinal image of eye.

Methods: This paper presents a simple and fast algorithm using Mathematical Morphology to find the fovea of fundus retinal image. The image for analysis is obtained from the DRIVE database. Also, this paper is enhanced to detect the Diabetic Retinopathy disease occurring in the eye.

Results: Detection of optic disc boundary becomes important for the diagnosis of glaucoma. The iterative curve evolution was stopped at the image boundaries where the energy was minimum.

Conclusion: The changes in the shape and size of the optic disc can be used to detect glaucoma and also cup ratio can be used as a measure of glaucoma.


Download data is not yet available.


K Duraiswamy. S Kavitha, S Karthikeyan. Neuroretinal rim quantification in fundus images to detect glaucomaâ€. Int J Computer Sci Network Security 2010;10:134-9.

Arturo Aquino, Diego Marin, Gegundez Arias, Manuel Emilio. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniquesâ€; 2010.

Gopal Datt Joshi. Optic Disk and Cup Boundary Detection Using Regional Information†Proceedings of the 2010 IEEE international conference on Biomedical Imaging: from nano to Macro; 2010.

MS Miri, A Mahloojifar. Retinal image analysis using curvelet transform and multi-structure elements morphology by reconstruction. Biomed Eng IEEE Transactions 2011;58:1183-92.

E Udayakumar. Automatic detection of diabetic retinopathy through optic disc using morphological methods. Asian J Pharm Clin Res Innovare Sci 2017;10:28-31.

S Santhi. Design and development of the smart glucose monitoring system. Int J Pharma Biosci 2017;8:631-8.

Atsushi Noud, Akira Sawad, Chisako Muramats, Hiroshi Fujita, Takeshi Hara, Yuji Hatanaka. Vertical cup-to-disc ratio measurement for diagnosis of glaucoma on fundus images. Proc SPIE 2010;7624:76243C-1.

Kavitha G, Pradeep Kumar AV, Prashanth C. Segmentation and grading of diabetic retinopathic exudates using error boost feature selection method. World Congress Information Communication Technologies; 2011. p. 518-23.

E Udayakumar. Certain investigation on the human body using various algorithms. Australian J Basic Appl Sci 2014;8:559-64.

X Jia, D Wong, F Yin, T Wong. Level-set based automatic cup-to-disc ratio determination using retinal fundus images in argali. Proc EMBC; 2008. p. 2266–9.

Jaspreet Kaur, HP Sinha. Automated localization of optic disc and macula from fundus images for automatic detection; 2012.

E Udayakumar. An identification f efficient vessel feature for endoscopic analysis. Res J Pharm Technol 2017;10:2633-6.



How to Cite

C., R., U. E., and Y. K. “DETECTION AND SEGMENTATION OF OPTIC DISC IN FUNDUS IMAGES”. International Journal of Current Pharmaceutical Research, vol. 10, no. 5, Sept. 2018, pp. 20-24, doi:10.22159/ijcpr.2018v10i5.29688.



Original Article(s)