Machine Vision and Applications

, Volume 23, Issue 6, pp 1129–1139 | Cite as

Layer extraction in rodent retinal images acquired by optical coherence tomography

  • József Molnár
  • Dmitry Chetverikov
  • Delia Cabrera DeBuc
  • Wei Gao
  • Gábor Márk Somfai
Original Paper


Optical coherence tomography (OCT) is a modern technique that allows for in vivo, fast, high-resolution 3D imaging. OCT can be efficiently used in eye research and diagnostics, when retinal images are processed to extract borders of retinal layers. In this paper, we present two novel algorithms for delineation of three main borders in rodent retinal images. The first, fast algorithm is based on row projections in a sliding window. It provides initial borders for a slower but more precise variational algorithm that iteratively refines the borders. The results obtained by the two algorithms are quantitatively evaluated by comparison to the borders manually extracted in a set of retinal images.


Optical coherence tomography Retinal images Retinal layers Segmentation Variational methods 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cabrera Fernández D., Salinas H.M., Puliafito C.A.: Automated detection of retinal layer structures on optical coherence tomography images. Optics Express 13(25), 10200–10216 (2005)CrossRefGoogle Scholar
  2. 2.
    Caselles V., Catté F., Coll T., Dibos F.: A geometric model for active contours in image processing. Numer. Math. 66(1), 1–31 (1993)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Caselles V., Kimmel R., Sapiro G.: Geodesic active contours. Int. J. Comput. Vision 22(1), 61–79 (1997)CrossRefMATHGoogle Scholar
  4. 4.
    Chan T.F., Vese L.A.: Contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefMATHGoogle Scholar
  5. 5.
    Fabritius T. et al.: Automated segmentation of the macula by optical coherence tomography. Optics Express 17(18), 15659–15669 (2009)CrossRefGoogle Scholar
  6. 6.
    Faugeras, O., Gomes, J., Keriven, R.: Variational principles in computational stereo. In: Geometric Level Set Methods in Imaging, Vision and Graphics. Springer, Berlin (2003)Google Scholar
  7. 7.
    Faugeras, O., Keriven, R.: Variational principles, surface evolution, PDE-s, level set methods, and the stereo problem. Technical report 3021, INRIA (1996)Google Scholar
  8. 8.
    Faugeras O., Keriven R.: Variational principles, surface evolution, PDE-s, level set methods, and the stereo problem. IEEE Trans. Image Process. 7, 336–344 (1998)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Fuller A.R. et al.: Segmentation of three-dimensional retinal image data. IEEE Trans. Visual. Comput. Graph. 13(6), 1719–1726 (2007)CrossRefGoogle Scholar
  10. 10.
    Garvin M.K. et al.: Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search. IEEE Trans. Med. Imaging 27(10), 1495–1505 (2008)CrossRefGoogle Scholar
  11. 11.
    Kajić V. et al.: Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis. Optics Express 18(14), 14730–14744 (2010)CrossRefGoogle Scholar
  12. 12.
    Kass M., Witkin A., Terzopoulos D.: Snakes: Active contour models. Int. J. Comput. Vision 1(4), 321–331 (1988)CrossRefGoogle Scholar
  13. 13.
    Leitgeb, R.A.: Paradigm shifts in optical coherence tomography. In: Proceedings of SPIE, vol. 6616, 661604 (2007)Google Scholar
  14. 14.
    Mishra A., Wong A., Bizheva K., Clausi D.A.: Intra-retinal layer segmentation in optical coherence tomography images. Optics Express 17(26), 23719–23728 (2009)CrossRefGoogle Scholar
  15. 15.
    Osher S., Fedkiw R.: Level Set Methods and Dynamic Implicit Surfaces. Springer, Berlin (2003)MATHGoogle Scholar
  16. 16.
    Puvanathasan P., Bizheva K.: Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set. Optics Express 15, 15747–15758 (2007)CrossRefGoogle Scholar
  17. 17.
    Ruggeri M. et al.: In vivo three-dimensional high-resolution imaging of rodent retina with spectral-domain optical coherence tomography. Invest. Ophthalmol. Visual Sci. 48(4), 1808 (2007)CrossRefGoogle Scholar
  18. 18.
    Ruggeri M. et al.: Retinal tumor imaging and volume quantification in mouse model using spectral-domain optical coherence tomography. Optics Express 17(5), 4074 (2009)CrossRefGoogle Scholar
  19. 19.
    Salinas H.M., Fernández D.C.: Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography. IEEE Trans. Med. Imaging 26(6), 761–771 (2007)CrossRefGoogle Scholar
  20. 20.
    Sethian J.A.: Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge (2002)Google Scholar
  21. 21.
    Shah D.M.J.: Optimal approximation by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 17, 577–685 (1989)Google Scholar
  22. 22.
    Szkulmowski M. et al.: Analysis of posterior retinal layers in spectral optical coherence tomography images of the normal retina and retinal pathologies. J. Biomed. Optics 12, 041207 (2007)CrossRefGoogle Scholar
  23. 23.
    Wong A., Mishra A., Bizheva K., Clausi D.A.: General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery. Optics Express 18(8), 8338–8352 (2010)CrossRefGoogle Scholar
  24. 24.
    Yazdanpanah, A., Hamarneh, G., Smith, B., Sarunic M.: Intra-retinal layer segmentation in optical coherence tomography using an active contour approach. Medical image computing and computer-assisted intervention, MICCAI 2009, pp. 649–656 (2009)Google Scholar
  25. 25.
    Zawadzki R.J. et al.: Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets. J. Biomed. Optics 12, 041206 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • József Molnár
    • 1
  • Dmitry Chetverikov
    • 1
  • Delia Cabrera DeBuc
    • 2
  • Wei Gao
    • 2
  • Gábor Márk Somfai
    • 3
  1. 1.MTA SZTAKI and Eötvös Loránd UniversityBudapestHungary
  2. 2.Bascom Palmer Eye Institute, Miller School of MedicineUniversity of MiamiMiamiUSA
  3. 3.Department of OphthalmologySemmelweis UniversityBudapestHungary

Personalised recommendations