Skip to main content
Springer Nature Link
Log in

A novel method for retinal optic disc detection using bat meta-heuristic algorithm

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Normally, the optic disc detection of retinal images is useful during the treatment of glaucoma and diabetic retinopathy. In this paper, the novel preprocessing of a retinal image with a bat algorithm (BA) optimization is proposed to detect the optic disc of the retinal image. As the optic disk is a bright area and the vessels that emerge from it are dark, these facts lead to the selected segments being regions with a great diversity of intensity, which does not usually happen in pathological regions. First, in the preprocessing stage, the image is fully converted into a gray image using a gray scale conversion, and then morphological operations are implemented in order to remove dark elements such as blood vessels, from the images. In the next stage, a bat algorithm (BA) is used to find the optimum threshold value for the optic disc location. In order to improve the accuracy and to obtain the best result for the segmented optic disc, the ellipse fitting approach was used in the last stage to enhance and smooth the segmented optic disc boundary region. The ellipse fitting is carried out using the least square distance approach. The efficiency of the proposed method was tested on six publicly available datasets, MESSIDOR, DRIVE, DIARETDB1, DIARETDB0, STARE, and DRIONS-DB. The optic disc segmentation average overlaps and accuracy was in the range of 78.5–88.2% and 96.6–99.91% in these six databases. The optic disk of the retinal images was segmented in less than 2.1 s per image. The use of the proposed method improved the optic disc segmentation results for healthy and pathological retinal images in a low computation time.

ᅟ

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Perez-Rovira A, Trucco E (2010) Contextual optic disc location in retinal fundus images. J Mod Opt 57(2):136–144

    Article  Google Scholar 

  2. Fleming AD, Goatman KA, Philip S, Olson JA, Sharp PF (2006) Automatic detection of retinal anatomy to assist diabetic retinopathy screening. Phys Med Biol 52(2):331

    Article  Google Scholar 

  3. Setiawan AW, Mengko TR, Santoso OS, Suksmono AB (2013) Color retinal image enhancement using CLAHE. In: ICT for Smart Society (ICISS), 2013 International Conference on, pp 1–3: IEEE

  4. Li H, Chutatape O (2001) Automatic location of optic disk in retinal images. In: Image Processing, 2001. Proceedings. 2001 International Conference on, vol 2, pp 837–840: IEEE

  5. Abed S, Al-Roomi SA, Al-Shayeji M (2016) Effective optic disc detection method based on swarm intelligence techniques and novel pre-processing steps. Appl Soft Comput 49:146–163

    Article  Google Scholar 

  6. Siddalingaswamy P, Prabhu G (2007) Automated detection of anatomical structures in retinal images. In: Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on, vol 3, pp 164–168: IEEE

  7. Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010), pp 65–74

  8. Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, Charton B, Klein JC (2014) Feedback on a publicly distributed image database: the Messidor database. Image Anal Stereol 33(3):231-234

    Article  Google Scholar 

  9. Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509

    Article  Google Scholar 

  10. Kauppi T et al. (2007) The DIARETDB1 diabetic retinopathy database and evaluation protocol. Medical Image Understanding and Analysis 2007:61-65.

  11. Kauppi T et al. (2006) DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms. Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland, vol 73

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

    Article  Google Scholar 

  13. Carmona EJ, Rincon M, Garcia-Feijoo J, Martinez-de-la-Casa JM (2008) Identification of the optic nerve head with genetic algorithms. Artif Intell Med 43(3):243–259

    Article  Google Scholar 

  14. Rahebi J, Hardalaç F (2016) A new approach to optic disc detection in human retinal images using the firefly algorithm. Med Biol Eng Comput 54(2–3):453–461

    Article  Google Scholar 

  15. Walter T, Klein JC, Massin P, Erginay A (2002) A contribution of image processing to the diagnosis of diabetic retinopathy—detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 21(10):1236–1243

    Article  Google Scholar 

  16. Seo J, Kim K, Kim J, Park K, Chung H (2004) Measurement of ocular torsion using digital fundus image. In: Engineering in Medicine and Biology Society, 2004. IEMBS'04. 26th Annual International Conference of the IEEE, vol 1, pp 1711–1713: IEEE

  17. Stapor K, Świtonski A, Chrastek R, Michelson G (2004) Segmentation of fundus eye images using methods of mathematical morphology for glaucoma diagnosis. Computational Science-ICCS 2004, pp 41–48

  18. Kande GB, Subbaiah PV, Savithri TS (2018) Segmentation of Exudates and Optic Disk in Retinal Images, presented at the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing

  19. Lupascu CA, Tegolo D, Di Rosa L (2008) Automated detection of optic disc location in retinal images. In: Computer-Based Medical Systems, 2008. CBMS'08. 21st IEEE International Symposium on, pp 17–22: IEEE

  20. Morales S, Naranjo V, Angulo U, Alcaniz M (2013) Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Trans Med Imaging 32(4):786–796

    Article  Google Scholar 

  21. Welfer D, Scharcanski J, Marinho DR (2013) A morphologic two-stage approach for automated optic disk detection in color eye fundus images. Pattern Recogn Lett 34(5):476–485

    Article  Google Scholar 

  22. Salazar-Gonzalez A, Kaba D, Li Y, Liu X (2014) Segmentation of the blood vessels and optic disk in retinal images. IEEE J Biomed Health Inform 18(6):1874–1886

    Article  Google Scholar 

  23. Roychowdhury S, Koozekanani DD, Kuchinka SN, Parhi KK (2016) Optic disc boundary and vessel origin segmentation of fundus images. IEEE J Biomed Health Inform 20(6):1562–1574

    Article  Google Scholar 

  24. Abdullah M, Fraz MM, Barman SA (2016) Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm. PeerJ 4:e2003

    Article  Google Scholar 

  25. Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S (2017) Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 20:70-79

    Google Scholar 

  26. Choukikar P, Patel AK, Mishra RS (2014) Segmenting the optic disc in retinal images using thresholding. International Journal of Computer Applications, 94(11):6-11

    Article  Google Scholar 

  27. Dias MA, Monteiro FC (2012) Optic disc detection using ant colony optimization. In: AIP Conference Proceedings, vol 1479, no 1, pp 798–801: AIP

  28. Bharkad S (2017) Automatic segmentation of optic disk in retinal images. Biomed Signal Process Control 31:483–498

    Article  Google Scholar 

  29. e Silva RRV, de Araujo FHD, dos Santos LMR, de Melo Souza Veras R, de Medeiros FNS (2016) Optic disc detection in retinal images using algorithms committee with weighted voting. IEEE Lat Am Trans 14(5):2446–2454

    Article  Google Scholar 

  30. Besenczi R, Szitha K, Harangi B, Csutak A, Hajdu A (2015) Automatic optic disc and optic cup detection in retinal images acquired by mobile phone. In: Image and Signal Processing and Analysis (ISPA), 2015 9th International Symposium on, pp 193–198: IEEE

  31. Hsiao H-K, Liu C-C, Yu C-Y, Kuo S-W, Yu S-S (2012) A novel optic disc detection scheme on retinal images. Expert Syst Appl 39(12):10600–10606

    Article  Google Scholar 

  32. Marin D, Gegundez-Arias ME, Suero A, Bravo JM (2015) Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput Methods Prog Biomed 118(2):173–185

    Article  Google Scholar 

  33. Van Loan CF (2008) Using the ellipse to fit and enclose data points. p 54. http://www.cs.cornell.edu/cv/otherpdf/ellipse.pdf.

Download references

Acknowledgments

The authors would like to thank the MESSIDOR, DRIVE, DIARETDB1, DIARETDB0, STARE, and DRIONS-DB program partners for facilitating their database.

Author information

Authors and Affiliations

  1. Altinbas University, Istanbul, Turkey

    Ahmad S. Abdullah & Yasa Ekşioğlu Özok

  2. Diyala University, Baqubah, Diyala, Iraq

    Ahmad S. Abdullah

  3. University of Turkish Aeronautical Association, Ankara, Turkey

    Javad Rahebi

Authors
  1. Ahmad S. Abdullah
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yasa Ekşioğlu Özok
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Javad Rahebi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ahmad S. Abdullah.

Ethics declarations

Competing interests

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdullah, A.S., Özok, Y.E. & Rahebi, J. A novel method for retinal optic disc detection using bat meta-heuristic algorithm. Med Biol Eng Comput 56, 2015–2024 (2018). https://doi.org/10.1007/s11517-018-1840-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-018-1840-1

Keywords