Advertisement

Method for Finding the Limits of Blood Vessel Landmarks in Eye Fundus Images Based on Distances in Graphs: Preliminary Results

  • Martynas PatašiusEmail author
  • Jūratė Šimkienė
  • Daivaras Sokas
  • Andrius Pranskūnas
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

Abstract

The paper proposes a method to process blood vessel landmarks in eye fundus images. The proposed method is based on finding a shortest cycle in a weighted graph, corresponding to a set of possible tracked blood vessel slices and paths between them. In turn, the paths are found by finding a shortest path in a weighted graph, taking gradients into account. The method has been tried out with DRIVE and IOSTAR databases.

Keywords

Eye fundus Blood vessels Graph theory 

Notes

Acknowledgements

This research was funded by a grant (No. S-MIP-17-16) from the Research Council of Lithuania.

References

  1. 1.
    Abbasi-Sureshjani, S., Smit-Ockeloen, I., Bekkers, E., Dashtbozorg, B., ter Haar Romeny, B.: Automatic detection of vascular bifurcations and crossings in retinal images using orientation scores. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE (2016).  https://doi.org/10.1109/isbi.2016.7493241
  2. 2.
    Aguiar, M., Castano, F., Trujillo, M.: Segmentation and detection of vascular bifurcations and crossings in retinal images. In: Communications in Computer and Information Science, pp. 432–443. Springer International Publishing (2018).  https://doi.org/10.1007/978-3-319-98998-3/_33
  3. 3.
    Al-Diri, B., Hunter, A.: Automated measurements of retinal bifurcations. In: O. Dössel, W.C. Schlegel (eds.) World Congress on Medical Physics and Biomedical Engineering, 7–12 September 2009, Munich, Germany, IFMBE Proceedings, vol. 25/XI, pp. 205–208. Springer (2009)Google Scholar
  4. 4.
    Bhuiyan, A., Nath, B., Chua, J., Ramamohanarao, K.: Automatic detection of vascular bifurcations and crossovers from color retinal fundus images. In: Third International IEEE Conference on Signal-Image Technologies and Internet-Based System 2007. SITIS 2007, pp. 711–718 (2007).  https://doi.org/10.1109/SITIS.2007.86
  5. 5.
    Bhuiyan, A., Nath, B., Ramamohanarao, K.: Detection and classification of bifurcation and branch points on retinal vascular network. In: 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA). IEEE (2012).  https://doi.org/10.1109/dicta.2012.6411742
  6. 6.
    Ghanaei, Z., Pourreza, H., Banaee, T.: Automatic graph-based method for classification of retinal vascular bifurcations and crossovers. In: 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE (2016).  https://doi.org/10.1109/iccke.2016.7802145
  7. 7.
    Kalaie, S., Gooya, A.: Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model. Comput. Methods Programs Biomed. 151, 139–149 (2017).  https://doi.org/10.1016/j.cmpb.2017.08.018CrossRefGoogle Scholar
  8. 8.
    Kang, J., Heo, S., Hyung, W.J., Lim, J.S., Lee, S.: 3D active vessel tracking using an elliptical prior. IEEE Trans. Image Process. 27(12), 5933–5946 (2018).  https://doi.org/10.1109/tip.2018.2862346MathSciNetCrossRefGoogle Scholar
  9. 9.
    Luo, T., Gast, T.J., Vermeer, T.J., Burns, S.A.: Retinal vascular branching in healthy and diabetic subjects. Invest. Ophthalmol. Vis. Sci. 58(5), 2685 (2017).  https://doi.org/10.1167/iovs.17-21653CrossRefGoogle Scholar
  10. 10.
    Mohan, V., Sundaramoorthi, G., Tannenbaum, A.: Tubular surface segmentation for extracting anatomical structures from medical imagery. IEEE Trans. Med. Imaging 29(12), 1945–1958 (2010).  https://doi.org/10.1109/tmi.2010.2050896CrossRefGoogle Scholar
  11. 11.
    Morales, S., Naranjo, V., Angulo, J., Legaz-Aparicio, A., Verdú-Monedero, R.: Retinal network characterization through fundus image processing: significant point identification on vessel centerline. Signal Process. Image Commun. 59, 50–64 (2017).  https://doi.org/10.1016/j.image.2017.03.013CrossRefGoogle Scholar
  12. 12.
    Patašius, M.: Akies dugno vaizdų automatinė analizė. Ph.D. thesis, Kauno technologijos universitetas (2010)Google Scholar
  13. 13.
    Patašius, M., Marozas, V., Jegelevičius, D., Lukoševičius, A.: Recursive algorithm for blood vessel detection in eye fundus images: preliminary results. In: Dössel, O., Schlegel, W.C. (eds.) World Congress on Medical Physics and Biomedical Engineering, 7–12 September 2009, Munich, Germany, IFMBE Proceedings, vol. 25/XI, pp. 212–215. Springer, Munich (2009)Google Scholar
  14. 14.
    Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004).  https://doi.org/10.1109/TMI.2004.825627CrossRefGoogle Scholar
  15. 15.
    Sutanty, E., Rahayu, D.A., Rodiah, Susetianingtias, D.T., Madenda, S.: Retinal blood vessel segmentation and bifurcation detection using combined filters. In: 2017 3rd International Conference on Science in Information Technology (ICSITech). IEEE (2017).  https://doi.org/10.1109/icsitech.2017.8257176
  16. 16.
    Treigys, P.: Grafinių oftalmologinių ir termovizinių duomenų analizės metodų kīrimas ir taikymas. Ph.D. thesis, Matematikos ir informatikos institutas (2010)Google Scholar
  17. 17.
    Tsai, C.L., Stewart, C.V., Tanenbaum, H.L., Roysam, B.: Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images. IEEE Trans. Inf Technol. Biomed. 8(2), 122–130 (2004).  https://doi.org/10.1109/TITB.2004.826733CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Martynas Patašius
    • 1
    • 2
    Email author
  • Jūratė Šimkienė
    • 3
  • Daivaras Sokas
    • 1
  • Andrius Pranskūnas
    • 3
  1. 1.Kaunas University of Technology, Institute of Biomedical EngineeringKaunasLithuania
  2. 2.Department of Applied InformaticsKaunas University of TechnologyKaunasLithuania
  3. 3.Department of Intensive CareLithuanian University of Health SciencesKaunasLithuania

Personalised recommendations