Attribute-Filtering and Knowledge Extraction for Vessel Segmentation

  • Benoît Caldairou
  • Nicolas Passat
  • Benoît Naegel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)


Attribute-filtering, relying on the notion of component-tree, enables to process grey-level images by taking into account high-level a priori knowledge. Based on these notions, a method is proposed for automatic segmentation of vascular structures from phase-contrast magnetic resonance angiography. Experiments performed on 16 images and validations by comparison to results obtained by two human experts emphasise the relevance of the method.


vessel segmentation mathematical morphology component-trees magnetic resonance angiography 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bouraoui, B., Ronse, C., Baruthio, J., Passat, N., Germain, P.: 3D segmentation of coronary arteries based on advanced Mathematical Morphology techniques. Computerized Medical Imaging and Graphics (in press) doi: 10.1016/j.compmedimag.2010.01.001Google Scholar
  2. 2.
    Breen, E.J., Jones, R.: Attribute openings, thinnings, and granulometries. Computer Vision and Image Understanding 64(3), 377–389 (1996)CrossRefGoogle Scholar
  3. 3.
    Caldairou, B., Naegel, B., Passat, N.: Segmentation of complex images based on component-trees: Methodological tools. In: Wilkinson, M.H.F., Roerdink, J.B.T.M. (eds.) International Symposium on Mathematical Morphology- ISMM 2009, 9th International Symposium, Proceedings. LNCS, vol. 5720, pp. 171–180. Springer, Heidelberg (2009)Google Scholar
  4. 4.
    Chen, L., Berry, M.W., Hargrove, W.W.: Using dendronal signatures for feature extraction and retrieval. International Journal of Imaging Systems and Technology 11(4), 243–253 (2000)CrossRefGoogle Scholar
  5. 5.
    Cline, H.E., Thedens, D.R., Meyer, C.H., Nishimura, D.G., Foo, T.K., Ludke, S.: Combined connectivity and a gray-level morphological filter in magnetic resonance coronary angiography. Magnetic Resonance in Medicine 43(6), 892–895 (2000)CrossRefGoogle Scholar
  6. 6.
    Cousty, J., Najman, L., Couprie, M., Clément-Guinaudeau, S., Goissen, T., Garot, J.: Segmentation of 4D cardiac MRI: Automated method based on spatio-temporal watershed cuts. Image and Vision Computing (in press), doi: 10.1016/j.imavis.2010.01.001Google Scholar
  7. 7.
    Gerig, G., Koller, T., Székely, G., Brechbühler, C., Kübler, O.: Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data. In: Barrett, H.H., Gmitro, A.F. (eds.) IPMI 1993. LNCS, vol. 687, pp. 94–111. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  8. 8.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2), 179–187 (1962)CrossRefzbMATHGoogle Scholar
  9. 9.
    Jones, R.: Connected filtering and segmentation using component trees. Computer Vision and Image Understanding 75(3), 215–228 (1999)CrossRefGoogle Scholar
  10. 10.
    Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Medical Image Analysis 13(6), 819–845 (2009)CrossRefGoogle Scholar
  11. 11.
    Naegel, B., Passat, N., Boch, N., Kocher, M.: Segmentation using vector-attribute filters: methodology and application to dermatological imaging. In: International Symposium on Mathematical Morphology - ISMM 2007, Proceedings, vol. 1, pp. 239–250. INPE (2007)Google Scholar
  12. 12.
    Naegel, B., Passat, N., Ronse, C.: Grey-level hit-or-miss transforms - Part II: Application to angiographic image processing. Pattern Recognition 40(2), 648–658 (2007)CrossRefzbMATHGoogle Scholar
  13. 13.
    Najman, L., Couprie, M.: Building the component tree in quasi-linear time. IEEE Transactions on Image Processing 15(11), 3531–3539 (2006)CrossRefGoogle Scholar
  14. 14.
    Ouzounis, G.K., Wilkinson, M.H.F.: Mask-based second-generation connectivity and attribute filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 990–1004 (2007)CrossRefGoogle Scholar
  15. 15.
    Salembier, P., Oliveras, A., Garrido, L.: Anti-extensive connected operators for image and sequence processing. IEEE Transactions on Image Processing 7(4), 555–570 (1998)CrossRefGoogle Scholar
  16. 16.
    Tankyevych, O., Talbot, H., Dokládal, P., Passat, N.: Direction-adaptive grey-level morphology. Application to 3D vascular brain imaging. In: International Conference on Image Processing - ICIP 2009, Proceedings, pp. 2261–2264. IEEE Signal Processing Society (2009)Google Scholar
  17. 17.
    Urbach, E.R., Boersma, N.J., Wilkinson, M.H.F.: Vector attribute filters. In: Proceedings of International Symposium on Mathematical Morphology - ISMM 2005, Proceedings. Computational Imaging and Vision, vol. 30, pp. 95–104. Springer SBM, Heidelberg (2005)Google Scholar
  18. 18.
    Urbach, E.R., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2), 272–285 (2007)CrossRefGoogle Scholar
  19. 19.
    Urbach, E.R., Wilkinson, M.H.F.: Shape-only granulometries and gray-scale shape filters. In: International Symposium on Mathematical Morphology - ISMM 2002, Proceedings, pp. 305–314. CSIRO Publishing (2002)Google Scholar
  20. 20.
    Vapnik, V.: Statistical Learning Theory. Wiley-Interscience, New York (1998)zbMATHGoogle Scholar
  21. 21.
    Wilkinson, M.H.F., Westenberg, M.A.: Shape preserving filament enhancement filtering. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 770–777. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Benoît Caldairou
    • 1
    • 2
  • Nicolas Passat
    • 1
  • Benoît Naegel
    • 3
  1. 1.Université de Strasbourg, LSIIT, UMR CNRS 7005France
  2. 2.Université de Strasbourg, LINC, UMR CNRS 7191France
  3. 3.Université Nancy 1, LORIA, UMR CNRS 7503France

Personalised recommendations