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Abstract

The multi–scale Frangi vesselness filter is an established tool in (retinal) vascular imaging. However, it cannot properly cope with crossings or bifurcations since it only looks for elongated structures. Therefore, we disentangle crossings/bifurcations via (multiple scale) invertible orientation scores and apply vesselness filters in this domain. This new method via scale–orientation scores performs considerably better at enhancing vessels throughout crossings and bifurcations than the Frangi version. Both methods are evaluated on a public dataset. Performance is measured by comparing ground truth data to the segmentation results obtained by basic thresholding and morphological component analysis of the filtered images.

Keywords

Multi-scale vesselness filters continuous wavelet transforms line detection gauge frames retinal imaging 

References

  1. 1.
    Ikram, M.K., Ong, Y.T., Cheung, C.Y., Wong, T.Y.: Retinal Vascular Caliber Measurements: Clinical Significance, Current Knowledge and Future Perspectives. Ophthalmologica 229(3), 125–136 (2013)CrossRefGoogle Scholar
  2. 2.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Budai, A., Bock, R., Maier, A., Hornegger, J., Michelson, G.: Robust Vessel Segmentation in Fundus Images. IJBI 2013 (2013)Google Scholar
  4. 4.
    Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: Retinal Vessel Segmentation Using AdaBoost. IEEE T-ITB 14(5), 1267–1274 (2010)Google Scholar
  5. 5.
    Duits, R., Felsberg, M., Granlund, G., ter Haar Romeny, B.: Image Analysis and Reconstruction using a Wavelet Transform Constructed from a Reducible Representation of the Euclidean Motion Group. IJCV 72(1), 79–102 (2007)CrossRefGoogle Scholar
  6. 6.
    Duits, R., Janssen, B., Bruurmijn, M., Florack, L., Van Assen, H.: Evolution Equations on Gabor Transforms and their Applications. In: ACHA (to appear, 2014)Google Scholar
  7. 7.
    Barbieri, D., Citti, G., Cocci, G., Sarti, A.: A cortical–inspired geometry for contour perception and motion integration. arXiv preprint arXiv:1301.3433 (2013)Google Scholar
  8. 8.
    Jacques, L., Antoine, J.P.: Multiselective pyramidal decomposition of images: wavelets with adaptive angular selectivity. International Journal of Wavelets, Multiresolution and Information Processing 5(05), 785–814 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Bekkers, E., Duits, R., Berendschot, T., ter Haar Romeny, B.: A Multi–Orientation Analysis Approach to Retinal Vessel Tracking. JMIV, 1–28 (2014)Google Scholar
  10. 10.
    Krause, M., Alles, R.M., Burgeth, B., Weickert, J.: Fast retinal vessel analysis. JRTIP, 1–10 (2013)Google Scholar
  11. 11.
    Fuehr, H.: Abstract Harmonic Analysis of Continuous Wavelet Transforms. Springer (2005)Google Scholar
  12. 12.
    Sharma, U., Duits, R.: Left-invariant evolutions of wavelet transforms on the Similitude Group. arXiv preprint arXiv:1306.1800 (2013)Google Scholar
  13. 13.
    Franken, E.: Enhancement of Crossing Elongated Structures in Images. PhD thesis, Technical University Eindhoven (2008)Google Scholar
  14. 14.
    Duits, R., Boscain, U., Rossi, F., Sachkov, Y.: Association Fields via Cuspless Sub–Riemannian Geodesics in SE (2). JMIV 1, 32 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Julius Hannink
    • 1
  • Remco Duits
    • 1
  • Erik Bekkers
    • 1
  1. 1.Department of Biomedical Engineering and Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands

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