Automatic Optic Disc and Fovea Detection in Retinal Images Using Super-Elliptical Convergence Index Filters

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)


This paper presents an automatic optic disc (OD) and fovea detection technique using an innovative super-elliptical filter (SEF). This filter is suitable for the detection of semi-elliptical convex shapes and as such it performs well for the OD localization. Furthermore, we introduce a setup for the simultaneous localization of the OD and fovea, in which the detection result of one landmark facilitates the detection of the other one. The evaluation is performed on 1200 images of the MESSIDOR dataset containing both normal and pathological cases of diabetic retinopathy (DR) and macular edema (ME). The proposed approach achieves success rates of 99.75 % and 98.87 % for the OD and fovea detection, respectively and outperforms or equals all known similar methods.


Retina Fovea Optic disc Convergence index filter Diabetic retinopathy 



The work is part of the Hé Programme of Innovation Cooperation, which is financed by the Netherlands Organization for Scientific Research (NWO), dossier No. 629.001.003.


  1. 1.
    MESSIDOR: Methods for Evaluating Segmentation and Indexing techniques Dedicated to Retinal Ophthalmology (2004).
  2. 2.
    Expert system for early automated detection of DR by analysis of digital retinal images project website (2012).
  3. 3.
    Aquino, A.: Establishing the macular grading grid by means of fovea centre detection using anatomical-based and visual-based features. Comput. Biol. Med. 55, 61–73 (2014)CrossRefGoogle Scholar
  4. 4.
    Aquino, A., Gegundez, M.E., Marin, D.: Automated optic disc detection in retinal images of patients with diabetic retinopathy and risk of macular edema. Int. J. Biol. Life Sci. 8(2), 87–92 (2012)Google Scholar
  5. 5.
    Bekkers, E., Duits, R., Romeny, B.H.: Optic nerve head detection via group correlations in multi-orientation transforms. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014, Part II. LNCS, vol. 8815, pp. 293–302. Springer, Heidelberg (2014)Google Scholar
  6. 6.
    Dashtbozorg, B., Mendonça, A.M., Campilho, A.: Optic disc segmentation using the sliding band filter. Comput. Biol. Med. 56, 1–12 (2015)CrossRefGoogle Scholar
  7. 7.
    Foracchia, M., Grisan, E., Ruggeri, A.: Luminosity and contrast normalization in retinal images. Med. Image Anal. 9(3), 179–190 (2005)CrossRefGoogle Scholar
  8. 8.
    Gegundez-Arias, M.E., Marin, D., Bravo, J.M., Suero, A.: Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques. Comput. Med. Imaging Graph. 37(5), 386–393 (2013)CrossRefGoogle Scholar
  9. 9.
    Giachetti, A., Ballerini, L., Trucco, E., Wilson, P.J.: The use of radial symmetry to localize retinal landmarks. Comput. Med. Imaging Graph. 37(5), 369–376 (2013)CrossRefGoogle Scholar
  10. 10.
    Kao, E.F., Lin, P.C., Chou, M.C., Jaw, T.S., Liu, G.C.: Automated detection of fovea in fundus images based on vessel-free zone and adaptive Gaussian template. Comput. Methods Prog. Biomed. 117(2), 92–103 (2014)CrossRefGoogle Scholar
  11. 11.
    Knudtson, M.D., Lee, K.E., Hubbard, L.D., Wong, T.Y., Klein, R., Klein, B.E.: Revised formulas for summarizing retinal vessel diameters. Curr. Eye Res. 27(3), 143–149 (2003)CrossRefGoogle Scholar
  12. 12.
    Kobatake, H., Hashimoto, S.: Convergence index filter for vector fields. IEEE Trans. Image Process. 8(8), 1029–1038 (1999)CrossRefGoogle Scholar
  13. 13.
    Lu, S.: Accurate and efficient optic disc detection and segmentation by a circular transformation. IEEE Trans. Med. Imaging 30(12), 2126–2133 (2011)CrossRefGoogle Scholar
  14. 14.
    Marin, D., Gegundez-Arias, M.E., Suero, A., Bravo, J.M.: Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput. Methods Prog. Biomed. 118(2), 173–185 (2015)CrossRefGoogle Scholar
  15. 15.
    Mendonça, A.M., Sousa, A., Mendonça, L., Campilho, A.: Automatic localization of the optic disc by combining vascular and intensity information. Comput. Med. Imaging Graph. 37(5), 409–417 (2013)CrossRefGoogle Scholar
  16. 16.
    Niemeijer, M., Abràmoff, M.D., Van Ginneken, B.: Fast detection of the optic disc and fovea in color fundus photographs. Med. Image Anal. 13(6), 859–870 (2009)CrossRefGoogle Scholar
  17. 17.
    Pereira, C.S., Fernandes, H., Mendonça, A.M., Campilho, A.C.: Detection of lung nodule candidates in chest radiographs. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 170–177. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Quigley, H.A., Brown, A.E., Morrison, J.D., Drance, S.M.: The size and shape of the optic disc in normal human eyes. Arch. Ophthalmol. 108(1), 51–57 (1990)CrossRefGoogle Scholar
  19. 19.
    Wei, J., Hagihara, Y., Kobatake, H.: Detection of cancerous tumors on chest x-ray images-candidate detection filter and its evaluation. In: IEEE International Conference on Image Processing, vol. 3, pp. 397–401 (1999)Google Scholar
  20. 20.
    Williams, T., Wilkinson, J.: Position of the fovea centralis with respect to the optic nerve head. Optom. Vis. Sci. 69(5), 369–377 (1992)CrossRefGoogle Scholar
  21. 21.
    Yu, H., Barriga, S., Agurto, C., Echegaray, S., Pattichis, M., Zamora, G., Bauman, W., Soliz, P.: Fast localization of optic disc and fovea in retinal images for eye disease screening. In: SPIE Medical Imaging, p. 796317. International Society for Optics and Photonics (2011)Google Scholar
  22. 22.
    Yu, H., Barriga, E.S., Agurto, C., Echegaray, S., Pattichis, M.S., Bauman, W., Soliz, P.: Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Trans. Inf. Technol. Biomed. 16(4), 644–657 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Sino-Dutch School for Biomedical and Information EngineeringNortheastern UniversityShenyangChina

Personalised recommendations