Optic Disc and Cup Segmentation from Color Fundus Photograph Using Graph Cut with Priors

  • Yuanjie Zheng
  • Dwight Stambolian
  • Joan O’Brien
  • James C. Gee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)


For automatic segmentation of optic disc and cup from color fundus photograph, we describe a fairly general energy function that can naturally fit into a global optimization framework with graph cut. Distinguished from most previous work, our energy function includes priors on the shape & location of disc & cup, the rim thickness and the geometric interaction of “disc contains cup”. These priors together with the effective optimization of graph cut enable our algorithm to generate reliable and robust solutions. Our approach is able to outperform several state-of-the-art segmentation methods, as shown by a set of experimental comparisons with manual delineations and a series of results of correlations with the assessments of a merchant-provided software from Optical Coherence Tomography (OCT) regarding several cup and disc parameters.


Optical Coherence Tomography Optic Disc Optic Nerve Head Active Contour Model Smoothness Term 
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  1. 1.
    Greaney, M.J., Hoffman, D.C., Garway-Heath, D.F., Nakla, M., Coleman, A.L., Caprioli, J.: Comparison of optic nerve imaging methods to distinguish normal eyes from those with glaucoma. Invest Ophthalmol Vis. Sci. 43, 140–145 (2002)Google Scholar
  2. 2.
    Lalonde, M., Beaulieu, M., Gagnon, L.: Fast and robust optic disc detection using pyramidal decomposition and hausdorff-based template matching. IEEE Transactions on Medical Imaging 20(11), 1193–1200 (2001)CrossRefGoogle Scholar
  3. 3.
    Abràmoff, M.D., Alward, W.L., Greenlee, E.C., Shuba, L., Kim, C.Y., Fingert, J.H., Kwon, Y.H.: Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features. Investigative Ophthalmology & Visual Science 48(4), 1665–1673 (2007)CrossRefGoogle Scholar
  4. 4.
    Joshi, G.D., Sivaswamy, J., Krishnadas, S.: Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Transactions on Medical Imaging 30(6), 1192–1205 (2011)CrossRefGoogle Scholar
  5. 5.
    Liu, J., Wong, D., Lim, J., Li, H., Tan, N., Zhang, Z., Wong, T., Lavanya, R.: Argali: an automatic cup-to-disc ratio measurement system for glaucoma analysis using level-set image processing. In: Lim, C.T., Goh, J.C.H. (eds.) ICBME 2008, Proceedings, vol. 23, pp. 559–562. Springer (2009)Google Scholar
  6. 6.
    Yu, H., Barriga, E., Agurto, C., Echegaray, S., Pattichis, M., Bauman, W., Soliz, P.: Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Transactions on Information Technology in Biomedicine 16(4), 644–657 (2012)CrossRefGoogle Scholar
  7. 7.
    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 Transactions on Medical Imaging 29(11), 1860–1869 (2010)CrossRefGoogle Scholar
  8. 8.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  9. 9.
    Ulen, J., Strandmark, P., Kahl, F.: An efficient optimization framework for multi-region segmentation based on lagrangian duality. IEEE Transactions on Medical Imaging 32(2), 178–188 (2013)CrossRefGoogle Scholar
  10. 10.
    Zhang, S., Zhan, Y., Metaxas, D.N.: Deformable segmentation via sparse representation and dictionary learning. Medical Image Analysis 16(7), 1385–1396 (2012)CrossRefGoogle Scholar
  11. 11.
    Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Towards robust and effective shape modeling: Sparse shape composition. Medical Image Analysis 16(1), 265–277 (2012)CrossRefGoogle Scholar
  12. 12.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J., et al.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yuanjie Zheng
    • 1
  • Dwight Stambolian
    • 2
  • Joan O’Brien
    • 2
  • James C. Gee
    • 1
  1. 1.Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of OphthalmologyPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaUSA

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