Simultaneous Segmentation of Retinal OCT Images Using Level Set

  • Bashir Isa DodoEmail author
  • Yongmin Li
  • Muhammad Isa Dodo
  • Xiaohui Liu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1211)


Medical images play a vital role in clinical diagnosis and treatments of various diseases. In the field of ophthalmology, Optical coherence tomography (OCT) has become an integral part of the non-invasive advanced eye examination by providing images of the retina in high resolution. Reliable identification of the retinal layers is necessary for the extraction of clinically useful information used for tracking the progress of medication and diagnosing various ocular diseases because changes to retinal layers highly correlate with the manifestation of eye diseases. Owing to the complexity of retinal structures and the cumbersomeness of manual segmentation, many computer-based methods are proposed to aid in extracting useful layer information. Additionally, image artefacts and inhomogeneity of pathological structures of the retina pose challenges by significantly degrading the performance of these computational methods. To handle some of these challenges, this paper presents a fully automated method for segmenting retinal layers in OCT images using a level set method. The method starts by establishing a specific Region of interest (ROI), which aids in handling over- and under-segmentation of the target layers by allowing only the layer and image features to influence the curve evolution. An appropriate level set initiation is devised by refining the edges from the image gradient. Then the prior understanding of the OCT image is utilised in constraining the evolution process to segment seven layers of the retina simultaneously. Promising experimental results have been achieved on 225 OCT images, which show the method converges close to the actual layer boundaries compared to the ground truth images.


Image segmentation Level set Evolution constrained optimisation Optical Coherence Tomography Medical image analysis 


  1. 1.
    Adhi, M., Duker, J.S.: Optical coherence tomography-current and future applications. Curr. Opin. Ophthalmol. 24(3), 213 (2013)CrossRefGoogle Scholar
  2. 2.
    Al-Ayyoub, M., AlZu’bi, S., Jararweh, Y., Shehab, M.A., Gupta, B.B.: Accelerating 3D medical volume segmentation using GPUs. Multimedia Tools Appl. 77(4), 4939–4958 (2018)CrossRefGoogle 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). Scholar
  4. 4.
    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). Scholar
  5. 5.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische mathematik 1(1), 269–271 (1959)MathSciNetCrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Dodo, B.I., Li, Y., Tucker, A., Kaba, D., Liu, X.: Retinal oct segmentation using fuzzy region competition and level set methods. In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 93–98. IEEE (2019)Google 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).
  9. 9.
    Dodo, B.I., Li, Y., Eltayef, K., Liu, X.: Min-Cut segmentation of retinal OCT images. In: Cliquet Jr., A., et al. (eds.) BIOSTEC 2018. CCIS, vol. 1024, pp. 86–99. Springer, Cham (2019). Scholar
  10. 10.
    Dodo., B.I., Li., Y., Liu., X., Dodo., M.I.: Level set segmentation of retinal oct images. In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING, vol. 2, pp. 49–56. INSTICC, SciTePress (2019).
  11. 11.
    Duan, J., Tench, C., Gottlob, I., Proudlock, F., Bai, L.: Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance. Pattern Recogn. 72, 158–175 (2017). Scholar
  12. 12.
    Garvin, M.K.: Automated 3-D segmentation and analysis of retinal optical coherence tomography images. PhD thesis - The University of Iowa (2008)Google Scholar
  13. 13.
    Huang, D., et al.: Optical coherence tomography. Science 254(5035), 1178–1181 (1991). (New York, N.Y)CrossRefGoogle Scholar
  14. 14.
    Jaffe, G.J.: OCT of the Macula: an expert provides a primer on useful scans, identifying artifacts and time domain vs. spectral domain technology. In: Reinal Physician, pp. 10–12 (2012)Google Scholar
  15. 15.
    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). Scholar
  16. 16.
    Lang, A., et al.: Retinal layer segmentation of macular OCT images using boundary classification. Biomed. Opt. Express 4(7), 1133–1152 (2013). Scholar
  17. 17.
    Liu, Y., Carass, A., Solomon, S.D., Saidha, S., Calabresi, P.A., Prince, J.L.: Multi-layer fast level set segmentation for macular oct. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1445–1448, April 2018Google 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).
  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.
    Raftopoulos, R., Trip, A.: The application of optical coherence tomography (OCT) in neurological disease. Adv. Clin. Neurosci. Rehabil. 12(2), 30–33 (2012)Google Scholar
  21. 21.
    Shi, Y., Karl, W.C.: A fast level set method without solving pdes [image segmentation applications]. In: Proceedings (ICASSP 2005) IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, vol. 2, pp. ii/97-ii100, March 2005.
  22. 22.
    Sun, Y., Zhang, T., Zhao, Y., He, Y.: 3D automatic segmentation method for retinal optical coherence tomography volume data using boundary surface enhancement. J. Innov. Opt. Heal. Sci. 9(02), 1650008 (2016)CrossRefGoogle Scholar
  23. 23.
    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). Scholar
  24. 24.
    Tsai, A., Yezzi, A., Willsky, A.S.: Curve evolution implementation of the mumford-shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image Process. 10(8), 1169–1186 (2001). Scholar
  25. 25.
    Vincent, L.: Morphological area openings and closings for grey-scale images. In: O, Y.L., Toet, A., Foster, D., Heijmans, H.J.A.M., Meer, P. (eds.) Shape in Picture. NATO ASI Series (Series F: Computer and Systems Sciences), vol. 126, pp. 197–208. Springer, Heidelberg (1994). Scholar
  26. 26.
    Wang, C., Wang, Y., Kaba, D., Wang, Z., Liu, X., Li, Y.: Automated layer segmentation of 3D macular images using hybrid methods. In: Zhang, Y.-J. (ed.) ICIG 2015. LNCS, vol. 9217, pp. 614–628. Springer, Cham (2015). Scholar
  27. 27.
    Wang, Q., Boyer, K.L.: The active geometric shape model: a new robust deformable shape model and its applications. Comput. Vis. Image Underst. 116(12), 1178–1194 (2012)CrossRefGoogle Scholar
  28. 28.
    Yazdanpanah, A., Hamarneh, G., Smith, B.R., Sarunic, M.V.: Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach. IEEE Trans. Med. Imaging 30, 484–496 (2011). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Bashir Isa Dodo
    • 1
    Email author
  • Yongmin Li
    • 1
  • Muhammad Isa Dodo
    • 2
  • Xiaohui Liu
    • 1
  1. 1.Brunel University LondonLondonUK
  2. 2.Katsina State Institute of Technology and ManagementKatsinaNigeria

Personalised recommendations