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Min-Cut Segmentation of Retinal OCT Images

  • Bashir Isa DodoEmail author
  • Yongmin Li
  • Khalid Eltayef
  • Xiaohui Liu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1024)

Abstract

Optical Coherence Tomography (OCT) is one of the most vital tools for diagnosing and tracking progress of medication of various retinal disorders. Many methods have been proposed to aid with the analysis of retinal images due to the intricacy of retinal structures, the tediousness of manual segmentation and variation from different specialists. However image artifacts, in addition to inhomogeneity in pathological structures, remain a challenge, with negative influence on the performance of segmentation algorithms. In this paper we present an automatic retinal layer segmentation method, which comprises of fuzzy histogram hyperbolization and graph cut methods. We impose hard constraints to limit search region to sequentially segment 8 boundaries and 7 layers of the retina on 150 OCT B-Sans images, 50 each from the temporal, nasal and center of foveal regions. Our method shows positive results, with additional tolerance and adaptability to contour variance and pathological inconsistence of the retinal structures in all regions.

Keywords

Retinal layer segmentation Optical Coherence Tomography Graph-cut Image analysis 

References

  1. 1.
    Baglietto, S., Kepiro, I.E., Hilgen, G., Sernagor, E., Murino, V., Sona, D.: Segmentation of retinal ganglion cells from fluorescent microscopy imaging. In: Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pp. 17–23 (2017).  https://doi.org/10.5220/0006110300170023
  2. 2.
    Baroni, M., Fortunato, P., La Torre, A.: Towards quantitative analysis of retinal features in optical coherence tomography. Med. Eng. Phys. 29(4), 432–441 (2007).  https://doi.org/10.1016/j.medengphy.2006.06.003CrossRefGoogle Scholar
  3. 3.
    Boyer, K.L., Herzog, A., Roberts, C.: Automatic recovery of the optic nervehead geometry in optical coherence tomography. IEEE Trans. Med. Imaging 25(5), 553–570 (2006).  https://doi.org/10.1109/TMI.2006.871417CrossRefGoogle Scholar
  4. 4.
    Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1(July), pp. 105–112 (2001).  https://doi.org/10.1109/ICCV.2001.937505
  5. 5.
    Cabrera Fernández, D., Salinas, H.M., Puliafito, C.A.: Automated detection of retinal layer structures on optical coherence tomography images. Opt. Express 13(25), 10200 (2005).  https://doi.org/10.1364/OPEX.13.010200CrossRefGoogle Scholar
  6. 6.
    Chiu, S.J., Li, X.T., Nicholas, P., Toth, C.A., Izatt, J.A., Farsiu, S.: Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation. Opt. Express 18(18), 19413–19428 (2010).  https://doi.org/10.1364/OE.18.019413CrossRefGoogle Scholar
  7. 7.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dodo, B.I., Li, Y., Eltayef, K., Liu, X.: Graph-cut segmentation of retinal layers from OCT images. In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies. BIOIMAGING, vol. 2, pp. 35–42. INSTICC, SciTePress (2018).  https://doi.org/10.5220/0006580600350042
  9. 9.
    Dodo, B.I., Li, Y., Liu, X.: Retinal OCT image segmentation using fuzzy histogram hyperbolization and continuous max-flow. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 745–750. IEEE (2017)Google Scholar
  10. 10.
    Ford, L.R., Fulkerson, D.R.: Maximal flow through a network. J. Can. de mathématiques 8, 399–404 (1956).  https://doi.org/10.4153/CJM-1956-045-5MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Garvin, M.K., Abràmoff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).  https://doi.org/10.1109/TMI.2009.2016958CrossRefGoogle Scholar
  12. 12.
    Haeker, M., Wu, X., Abràmoff, M., Kardon, R., Sonka, M.: Incorporation of regional information in optimal 3-D graph search with application for intraretinal layer segmentation of optical coherence tomography images. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 607–618. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-73273-0_50CrossRefGoogle Scholar
  13. 13.
    Huang, D., et al.: Optical coherence tomography. Sci. (New York, N.Y.) 254(5035), 1178–1181 (1991).  https://doi.org/10.1126/science.1957169 CrossRefGoogle Scholar
  14. 14.
    Kaba, D., et al.: Retina layer segmentation using kernel graph cuts and continuous max-flow. Opt. Express 23(6), 7366–7384 (2015).  https://doi.org/10.1364/OE.23.007366CrossRefGoogle Scholar
  15. 15.
    Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004).  https://doi.org/10.1109/TPAMI.2004.1262177CrossRefzbMATHGoogle Scholar
  16. 16.
    Koozekanani, D., Boyer, K., Roberts, C.: Retinal thickness measurements from optical coherence tomography using a Markov boundary model. IEEE Trans. Med. Imaging 20(9), 900–916 (2001).  https://doi.org/10.1109/42.952728CrossRefGoogle Scholar
  17. 17.
    Lang, A., et al.: Retinal layer segmentation of macular OCT images using boundary classification. Biomed. Opt. Express 4(7), 1133–1152 (2013).  https://doi.org/10.1364/BOE.4.001133CrossRefGoogle Scholar
  18. 18.
    Lu, S., Yim-liu, C., Lim, J.H., Leung, C.K.S., Wong, T.Y.: Automated layer segmentation of optical coherence tomography images. In: Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011, vol. 1, no. 10, pp. 142–146 (2011).  https://doi.org/10.1109/BMEI.2011.6098329
  19. 19.
    Novosel, J., Vermeer, K.A., Thepass, G., Lemij, H.G., Vliet, L.J.V.: Loosely coupled level sets for retinal layer segmentation in optical coherence tomography. In: IEEE 10th International Symposium on Biomedical Imaging, pp. 998–1001 (2013)Google Scholar
  20. 20.
    Salazar-Gonzalez, A., Kaba, D., Li, Y., Liu, X.: Segmentation of the blood vessels and optic disk in retinal images. IEEE J. Biomed. Health Inform. 18(6), 1874–1886 (2014)CrossRefGoogle Scholar
  21. 21.
    Salazar-Gonzalez, A., Li, Y., Liu, X.: Automatic graph cut based segmentation of retinal optic disc by incorporating blood vessel compensation. J. Artif. Intell. Soft Comput. Res. 2(3), 235–245 (2012)Google Scholar
  22. 22.
    Salazar-Gonzalez, A.G., Li, Y., Liu, X.: Retinal blood vessel segmentation via graph cut. In: International Conference on Control Automation Robotics and Vision, pp. 225–230 (2010)Google Scholar
  23. 23.
    Seheult, A., Greig, D., Porteous, B.: Exact maximum a posteriori estimation for binary images. J. R. Stat. Soc. 51(2), 271–279 (1989)Google Scholar
  24. 24.
    Tian, J., Varga, B., Somfai, G.M., Lee, W.H., Smiddy, W.E., DeBuc, D.C.: Real-time automatic segmentation of optical coherence tomography volume data of the macular region. PLoS ONE 10(8), 1–20 (2015).  https://doi.org/10.1371/journal.pone.0133908CrossRefGoogle Scholar
  25. 25.
    Tizhoosh, H.R., Krell, G., Michaelis, B.: Locally adaptive fuzzy image enhancement. In: Reusch, B. (ed.) Fuzzy Days 1997. LNCS, vol. 1226, pp. 272–276. Springer, Heidelberg (1997).  https://doi.org/10.1007/3-540-62868-1_118 CrossRefGoogle Scholar
  26. 26.
    Wang, C., Kaba, D., Li, Y.: Level set segmentation of optic discs from retinal images. J. Med. Syst. 4(3), 213–220 (2015)Google Scholar
  27. 27.
    Zhang, Y.-J. (ed.): ICIG 2015. LNCS, vol. 9217. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-21978-3CrossRefGoogle Scholar
  28. 28.
    Wang, C., Wang, Y., Li, Y.: Automatic choroidal layer segmentation using Markov random field and level set method. IEEE J. Biomed. Health Inform. 21, 1694–1702 (2017)CrossRefGoogle Scholar
  29. 29.
    Yuan, J., Bae, E., Tai, X.C., Boykov, Y.: A study on continuous max- flow and min-cut approaches. In: 2010 IEEE Conference, vo. 7, pp. 2217–2224 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bashir Isa Dodo
    • 1
    Email author
  • Yongmin Li
    • 1
  • Khalid Eltayef
    • 1
  • Xiaohui Liu
    • 1
  1. 1.Brunel University LondonLondonUK

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