Using Blood Vessels Location Information in Optic Disk Segmentation

  • A. S. Semashko
  • A. S. Krylov
  • A. S. Rodin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6979)


In this paper we present an approach to achieve high accuracy of optic disk segmentation using information on the location of blood vessels (vessel map). Morphological preprocessing is employed to remove the vessels from the image and to compute the vessel map. Vessel map is combined with edge map to obtain robust initial approximation of OD boundary using circular Hough transform. We use this approximation to build 2D weight function for the edge map, which is then used in the active contour model. We introduce an additional step to perform correction of the contour; in this step, the active contour model includes pressure forces and soft elliptical constraint. Vessel map is used in calculation of the ellipse parameters and pressure values. The method was tested on 1240 publicly available retinal images, and manual labeling of the disk boundary by medical experts was used to assess its accuracy and compare it with other optic disk segmentation methods.


Optic Disk Active Contour Retinal Image Active Contour Model Morphological Closing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Aquino, A., Gegundez-Arias, M., Marin, D.: Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection and Feature Extraction Techniques. IEEE Transactions on Medical Imaging 29, 1860–1869 (2010)CrossRefGoogle Scholar
  2. 2.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Processing 10(2), 266–277 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    Cohen, L.: On active contour models and balloons. CVGIP: Image Understanding 53(2), 211–218 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Duanggate, C., Uyyanonvara, B., Makhanov, S.S., Barman, S., Williamson, T.: Parameter-free optic disc detection. Computerized Medical Imaging and Graphics 35(1), 51–63 (2011)CrossRefGoogle Scholar
  5. 5.
    Expert system for early automated detection of DR by analysis of digital retinal images project website. Univ. Huelva, Huelva,
  6. 6.
    Joshi, G.D., Gautam, R., Sivaswamy, J., Krishnadas, S.R.: Robust optic disk segmentation from colour retinal images. In: Proceedings of the 7th Indian Conference on Computer Vision, Graphics and Image Processing, pp. 330–336 (2010)Google Scholar
  7. 7.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefzbMATHGoogle Scholar
  8. 8.
    Lalonde, M., Beaulieu, M., Gagnon, L.: Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans. Med. Imaging 20(11), 1193–1200 (2001)CrossRefGoogle Scholar
  9. 9.
    Li, H., Chutatape, O.: Boundary detection of optic disk by a modified ASM method. Pattern Recognition 36(9), 2093–2104 (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., Kennedy, L.: Optic nerve head segmentation. IEEE Trans. Medical Imaging 23(2), 256–264 (2004)CrossRefGoogle Scholar
  11. 11.
    Mendels, F., Heneghan, C., Thiran, J.: Identification of the optic disk boundary in retinal images using active contours. In: Proc. Irish Machine Vision and Image Processing Conference on Cerebrovascular Diseases, pp. 103–115. IEEE, Los Alamitos (1999)Google Scholar
  12. 12.
    MESSIDOR: Digital Retinal Images, MESSIDOR TECHNO-VISION Project, France,
  13. 13.
    Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Colour morphology and snakes for optic disc localisation. In: 6th MIUA Conference, pp. 21–24 (2002)Google Scholar
  14. 14.
    Siddalingaswamy, P., Gopalakrishna, P.: Automatic Localization and Boundary Detection of Optic Disc Using Implicit Active Contours. International Journal of Computer Applications 1(6), 1–5 (2010)CrossRefGoogle Scholar
  15. 15.
    Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Medical Imaging 23(4), 501–509 (2004)CrossRefGoogle Scholar
  16. 16.
    Tang, Y., Li, X., von Freyberg, A., Goch, G.: Automatic segmentation of the papilla in a fundus image based on the C-V model and a shape restraint. In: Proc. ICPR, pp. 183–186 (2006)Google Scholar
  17. 17.
    Winder, R.J., Morrow, P.J., McRitchie, I.N., Bailie, J.R., Hart, P.M.: Algorithms for digital image processing in diabetic retinopathy. Computerized Medical Imaging and Graphics 33(8), 608–622 (2009)CrossRefGoogle Scholar
  18. 18.
    Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Processing 7, 359–369 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Xu, J., Chutatape, O., Sung, E., Zheng, C., Chew, P.: Optic disk feature extraction via modified deformable model technique for glaucoma analysis. Pattern Recognition 40(7), 2063–2076 (2007)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • A. S. Semashko
    • 1
  • A. S. Krylov
    • 1
  • A. S. Rodin
    • 2
  1. 1.Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and CyberneticsLomonosov Moscow State UniversityRussia
  2. 2.Ophthalmology Chair, Faculty of Fundamental MedicineLomonosov Moscow State UniversityRussia

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