Skip to main content
SpringerLink
Log in

High speed detection of optical disc in retinal fundus image

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The first task in any retinal fundus image processing is to detect the optical disc, as this is the prime location in a fundus image from where all retinal blood vessels originate. In this paper, a faster method to detect retinal optical disc is proposed that uses mean intensity value of retinal image to detect the center of optical disc, which can be used in retinal image–based person authentication system or retinal disease diagnosis. A candidate-based approach on green channel of RGB fundus image is used to detect optical disc center location. The system has been successfully tested on several publicly available standard databases, namely: DRIVE, messidor, VARIA, VICAVR and DIARETDB_01 and produced 97.5, 97.8, 94, 93.1 and 86.5 % accuracies, respectively. It is observed that if lower recognition accuracy is accepted (from 100 to 97.5 %) on DRIVE database, the detection speed increases from 7 to 2 s per image, which is faster than any other previous methods with such high accuracies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. Mydriatic—usually images for retinal disease diagnosis are taken using some medication that helps with the dilation of the pupil. If the medication is used to artificially dilate the pupil of eye, and then, the image is taken then it will be mydriatic image.

References

  1. Amin, M.A., Yan, H.: High speed detection of retinal blood vessels in fundus image using phase congruency. Soft Comput. 15(6), 1217–1230 (2010)

    Article  Google Scholar 

  2. Bevilacqua, V., Cariello, L., Giannini Giuseppe Mastronardi, M., Santarcangelo, V., Scaramuzzi, R., Troccoli, A.: A comparison between a geometrical and an ANN based method for retinal bifurcation points extraction. J. Univers. Comput. Sci. 15(13), 2608–2621 (2009)

    Google Scholar 

  3. Bevilacqua, V., Cariello, L., Cambo, S., Daleno, D., Mastronardi, G.: Retinal fundus hybrid analysis based on soft computing algorithms. Commun. SIMAI Congr. ISSN: 1827–9015 2, 1–8 (2007)

    Google Scholar 

  4. Bevilacqua, V., Carnimeo, L., Mastronardi, G., Santarcangelo, V., Scaramuzzi, R.: On the comparison of NN-based architectures for diabetic damage detection in retinal images. J. Circuits Syst. Comput. 18(8), 1369–1380 (2009)

    Article  Google Scholar 

  5. Bevilacqua, V., Mastronardi, G., Colaninno, A., D’Addabbo, A.: Retinal images processing using genetic algorithm and maximum likelihood method. In: Proceedings of IASTED ACST: Advances in Computer Science and Technology. Virgin Islands, USA: IASTED, November vol. 22–24, pp. 277–280 (2004)

  6. Youssif, A.A.H.A.R., Ghalwash, A.Z., Ghoneim, A.A.S.A.R.: Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. In: IEEE Trans. Med. Imaging 27(1), 11–18 (2008)

  7. Ortega, M., Marino, C., Penedo, M.G., Blanco, M., Gonzalez, F.: Biometric authentication using digital retinal images. In: Proceedings of the 5th International WSEAS Conference Applied Computer Science, Hangzhou, China, April 16–18, pp. 422–427 (2006)

  8. Sekhar, S., Al-Nuaimy, W., Nandi, A.K.: Automated localization of retinal optic disc using hough transform. In: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, May 14–17, pp. 1577–1580 (2008)

  9. Farzin, H., Abrishami-Moghaddam H., Moin M.: A novel retinal identification system. EURASIP J. Adv. Signal Process. 2008, Article Id. 280635 (2008)

  10. Ying, H., Zhang, M., Liu J.: Fractal-based automatic localization and segmentation of optic disc in retinal images. In: Proceedings of the 29th Annual International Conference on the IEEE Engineering in Medicine and Biology Society. EMBS, August 22–26, pp. 4139–4141 (2007)

  11. Zhang, M., Liu, J.: Directional local Contrast based blood vessel detection in retinal images. In: Proceedings of the IEEE International Conference on Image Processing, September 16, pp. 317–320 (2007)

  12. Lalonde, M., Beaulieu, M., Gagnon, L.: Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. Proc. IEEE Trans. Med. Imaging 20(11), 1193–1200 (Nov. 2001)

    Google Scholar 

  13. Foracchia, M., Grisan, E., Ruggeri, A.: Detection of optic disc in retinal images by means of a geometrical model of vessel structure. Proc. IEEE Trans. Med. Imaging 23(10), 1189–1195 (Oct. 2004)

    Google Scholar 

  14. Sopharak, A., Thet New, K., Aye Moe, Y.N., Dailey, M., Uyyanonvara, B.: Automatic exudate detection with a naive Bayes classifier. In: Proceedings of the International Conference on Embedded Systems and Intelligent Technology, Grand Mercure Fortune Hotel, pp. 139–142. Bangkok, Thailand (2008)

  15. Otsu, N.: A threshold selection method from gray-level histograms. Proc. IEEE Trans. Syst. Mach. Cybern. 9(1), 62–66 (Jan. 1979)

  16. Li, H., Chutatape, O.: Automatic location of optic disc in retinal images. In: Proceedings of the International Conference on Image Processing, October 7–10, pp. 837–840 (2011)

  17. DRIVE: Digital Retinal Image for Vessel Extraction, http://www.isi.uu.nl/Research/Databases/DRIVE

  18. DIARETDB1:Standard Diabetic Retinopathy Database, http://www2.it.lut.fi/project/imageret/diaretdb1/index.html

  19. VARIA database, http://www.varpa.es/varia.html

  20. VICAVR Database, http://www.varpa.es/vicavr.html

  21. MESSIDOR: Digital Retinal Images, http://messidor.crihan.fr/download.php

  22. Ravishankar, S., Jain, A., Mittal, A.: Automated feature extraction for early detection of diabetic retinopathy in fundus images. CVPR In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 20–25, pp. 210–217 (2009)

  23. Hoover, A., Goldbaum, M.: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. Proc. IEEE Trans. Med. Imaging 22(8), 951–958 (Aug. 2003)

    Google Scholar 

  24. Harangi, B., Qureshi, R.J., Csutak, A., Peto, T., Hajdu, A.: Automatic detection of the optic disc using majority voting in a collection of optic disc detectors. In: Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, April 14–17, pp. 1329–1332 (2010)

Download references

Acknowledgments

The research is supported by the G4S Bangladesh (http://www.g4s.com.bd). The authors would like to thank anonymous reviewers for their helpful comments and Mr. Saami Rahman for proof reading.

Author information

Authors and Affiliations

  1. Computer Vision and Cybernetic Group, SECS, Independent University Bangladesh (IUB), Dhaka, Bangladesh

    M. Islamuddin Ahmed & M. Ashraful Amin

  2. Bio-computing Lab, Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong

    M. Ashraful Amin

Authors
  1. M. Islamuddin Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Ashraful Amin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Ashraful Amin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ahmed, M.I., Amin, M.A. High speed detection of optical disc in retinal fundus image. SIViP 9, 77–85 (2015). https://doi.org/10.1007/s11760-012-0412-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-012-0412-3

Keywords

Search

Navigation