Automatic optic disk detection and segmentation by variational active contour estimation in retinal fundus images

  • Syed S. NaqviEmail author
  • Nayab Fatima
  • Tariq M. Khan
  • Zaka Ur Rehman
  • M. Aurangzeb Khan
Original Paper


Computer-aided optic disk (OD) detection and segmentation is at the heart of modern fundus image screening systems for early detection and diagnosis of glaucoma and diabetic retinopathy. Algorithms that generalize well on fundus images with diseases, as well as screening images, are of utmost importance. This paper presents a method based on OD homogenization and subsequent contour estimation to address the challenges of OD detection in cases where either the OD boundary is discontinuous or very smooth, due to the presence of disease. This is achieved by local Laplacian filtering-based inpainting of the major vascular structure to complete the OD boundary and gradient-independent active contour estimation for unconstrained OD boundary detection. Experimental evaluation of the proposed method on three benchmark datasets and quantitative comparison with the best performing state-of-the-art methods in terms of four quantitative measures demonstrate its competitive performance and reliability for OD screening.


Optic disk Inpainting Variational active contour Contour estimation Fundus image screening 



There was no funding received for this work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Yau, J.W., Rogers, S.L., Kawasaki, R., Lamoureux, E.L., Kowalski, J.W., Bek, T., Chen, S.J., Dekker, J.M., Fletcher, A., Grauslund, J., et al.: Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35(3), 556–564 (2012)CrossRefGoogle Scholar
  2. 2.
    Soomro, T.A., Khan, T.M., Khan, M.A.U., Gao, J., Paul, M., Zheng, L.: Impact of ica-based image enhancement technique on retinal blood vessels segmentation. IEEE Access 6, 3524–3538 (2018)CrossRefGoogle Scholar
  3. 3.
    Khowaja, S.A., Khuwaja, P., Ismaili, I.A.: A framework for retinal vessel segmentation from fundus images using hybrid feature set and hierarchical classification. Signal Image Video Process (2018).
  4. 4.
    Calimeri, F., Marzullo, A., Stamile, C., Terracina, G.: Optic disc detection using fine tuned convolutional neural networks. In: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 69–75 (2016)Google Scholar
  5. 5.
    Soomro, T.A., Khan, M.A.U., Gao, J., Khan, T.M., Paul, M.: Contrast normalization steps for increased sensitivity of a retinal image segmentation method. Signal Image Video Process. 11(8), 1509–1517 (2017). CrossRefGoogle Scholar
  6. 6.
    Morales, S., Naranjo, V., Angulo, J., Alcañiz, M.: Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Trans. Med. Imag. 32(4), 786–796 (2013)CrossRefGoogle Scholar
  7. 7.
    Septiarini, A., Harjoko, A., Pulungan, R., Ekantini, R.: Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation. Signal Image Video Process. 11(5), 945–952 (2017)CrossRefGoogle Scholar
  8. 8.
    Guo, X., Li, Q., Sun, C.: Automatic localization of optic disk based on texture orientation voting. Signal Image Video Process. 11(6), 1115–1122 (2017)CrossRefGoogle Scholar
  9. 9.
    Park, M., Jin, J.S., Luo, S.: Locating the optic disc in retinal images. In: 2006 International Conference on Computer Graphics, Imaging and Visualisation, pp. 141–145 (2006)Google Scholar
  10. 10.
    Aquino, A., Gegúndez-Arias, M.E., Marín, D.: Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans. Med. Imag. 29(11), 1860–1869 (2010)CrossRefGoogle Scholar
  11. 11.
    Lalonde, M., Beaulieu, M., Gagnon, L.: Fast and robust optic disc detection using pyramidal decomposition and hausdorff-based template matching. IEEE Trans. Med. Imag. 20(11), 1193–1200 (2001)CrossRefGoogle Scholar
  12. 12.
    Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Comparison of colour spaces for optic disc localisation in retinal images. In: 16th International Conference on Pattern Recognition, 2002. Proceedings, vol. 1, pp. 743–746 (2002)Google Scholar
  13. 13.
    Li, H., Chutatape, O.: Automated feature extraction in color retinal images by a model based approach. IEEE Trans. Biomed. Eng. 51(2), 246–254 (2004)CrossRefGoogle Scholar
  14. 14.
    Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., Kennedy, L.: Optic nerve head segmentation. IEEE Trans. Med. Imag. 23(2), 256–264 (2004)CrossRefGoogle Scholar
  15. 15.
    Walter, T., Klein, J.C., Massin, P., Erginay, A.: 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. Imag. 21(10), 1236–1243 (2002)CrossRefGoogle Scholar
  16. 16.
    Welfer, D., Scharcanski, J., Kitamura, C.M., Dal Pizzol, M.M., Ludwig, L.W., Marinho, D.R.: Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach. Comput. Biol. Med. 40(2), 124–137 (2010)CrossRefGoogle Scholar
  17. 17.
    Abramoff, M.D., Niemeijer, M.: The automatic detection of the optic disc location in retinal images using optic disc location regression. In: Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE, pp. 4432–4435 (2006)Google Scholar
  18. 18.
    Fan, Z., Rong, Y., Cai, X., Lu, J., Li, W., Lin, H., Chen, X.: Optic disk detection in fundus image based on structured learning. IEEE J. Biomed. Health Inform. 22(1), 224–234 (2018)CrossRefGoogle Scholar
  19. 19.
    Johnson, R., Fu, A., McDonald, H., Jumper, J., Ai, E., Cunningham, E., Lujan, B.: Fluorescein Angiography: Basic Principles and Interpretation, vol. 1. Elsevier Inc., Amsterdam (2012). Google Scholar
  20. 20.
    Yu, H., Agurto, C., Barriga, S., Nemeth, S.C., Soliz, P., Zamora, G.: Automated image quality evaluation of retinal fundus photographs in diabetic retinopathy screening. In: 2012 IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 125–128 (2012)Google Scholar
  21. 21.
    Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1674–1682 (2016)Google Scholar
  22. 22.
    Itti, L., Koch, C.: Comparison of feature combination strategies for saliency-based visual attention systems. In: Proceedings of SPIE Human Vision and Electronic Imaging IV (HVEI’99), San Jose, CA, vol. 3644, pp. 473–82 (1999)Google Scholar
  23. 23.
    Paris, S., Hasinoff, S.W., Kautz, J.: Local laplacian filters: edge-aware image processing with a laplacian pyramid. Commun. ACM 58(3), 81–91 (2015)CrossRefGoogle Scholar
  24. 24.
    Almazroa, A., Burman, R., Raahemifar, K., Lakshminarayanan, V.: Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J. Ophthalmol. (2015).
  25. 25.
    Zhang, Z., Liu, J., Cherian, N.S., Sun, Y., Lim, J.H., Wong, W.K., Tan, N.M., Lu, S., Li, H., Wong, T.Y.: Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1441–1444 (2009)Google Scholar
  26. 26.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefzbMATHGoogle Scholar
  27. 27.
    Dai, B., Wu, X., Bu, W.: Optic disc segmentation based on variational model with multiple energies. Pattern Recognit. 64, 226–235 (2017)CrossRefGoogle Scholar
  28. 28.
    Xu, J., Chutatape, O., Chew, P.: Automated optic disk boundary detection by modified active contour model. IEEE Trans. Biomed. Eng. 54(3), 473–482 (2007)CrossRefGoogle Scholar
  29. 29.
    Carmona, E.J., Rincón, M., García-Feijoó, J., de-la Casa, J.M.M.: Identification of the optic nerve head with genetic algorithms. Artif. Intell. Med. 43(3), 243–259 (2008)CrossRefGoogle Scholar
  30. 30.
    M., T.V.: Messidor: Digital retinal images France (2008).
  31. 31.
    Abdullah, M., Fraz, M.M., Barman, S.A.: Localization and segmentation of optic disc in retinal images using circular hough transform and grow-cut algorithm. PeerJ 4, e2003 (2016)CrossRefGoogle Scholar
  32. 32.
    Zahoor, M.N., Fraz, M.M.: Fast optic disc segmentation in retina using polar transform. IEEE Access 5, 12293–12300 (2017)CrossRefGoogle Scholar
  33. 33.
    dos Santos Ferreira, M.V., de Carvalho Filho, A.O., de Sousa, A.D., Silva, A.C., Gattass, M.: Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma. Expert Syst. Appl. 110, 250–263 (2018)CrossRefGoogle Scholar
  34. 34.
    Al-Bander, B., Al-Nuaimy, W., Williams, B.M., Zheng, Y.: Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc. Biomed. Signal Process. Control 40, 91–101 (2018)CrossRefGoogle Scholar
  35. 35.
    Fu, H., Cheng, J., Xu, Y., Wong, D.W.K., Liu, J., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imag. 37(7), 1597–1605 (2018)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringCOMSATS University IslamabadIslamabadPakistan
  2. 2.School of Computing and CommunicationsLancaster UniversityLancasterUK

Personalised recommendations