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Segmenting 3D Branching Tubular Structures Using Cores

  • Yonatan Fridman
  • Stephen M. Pizer
  • Stephen Aylward
  • Elizabeth Bullitt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2879)

Abstract

Blood vessels and other anatomic objects in the human body can be described as trees of branching tubes. The focus of this paper is the extraction of the branching geometry in 3D, as well as the extraction of the tubes themselves via skeletons computed as cores. Cores are height ridges of a graded measure of medial strength called medialness, which measures how much a given location resembles the middle of an object as indicated by image intensities. The methods presented in this paper are evaluated on synthetic images of branching tubular objects as well as on blood vessels in head MR angiogram data. Results show impressive resistance to noise and the ability to detect branches spanning a variety of widths and branching angles.

Keywords

Image Noise Tubular Structure Synthetic Image Core Termination Medial Atom 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bullitt, E., Aylward, S.R., Smith, K., Mukherji, S., Jiroutek, M., Muller, K.: Symbolic description of intracerebral vessels segmented from magnetic resonance angiograms and evaluation by comparison with X-ray angiograms. Medical Image Analysis 5, 157–169 (2001)CrossRefGoogle Scholar
  2. 2.
    Fritsch, D.S., Eberly, D., Pizer, S.M., McAuliffe, M.J.: Stimulated cores and their applications in medical imaging. In: Bizais, Y., Barillot, C., DiPaola, R. (eds.) Information Processing in Medical Imaging. Kluwer Series in Computational Imaging and Vision, pp. 365–368 (1995)Google Scholar
  3. 3.
    Furst, J.D.: Height Ridges of Oriented Medialness. Ph.D. Dissertation, Department of Computer Science, University of North Carolina at Chapel Hill (1999)Google Scholar
  4. 4.
    Morse, B.S., Pizer, S.M., Puff, D.T., Gu, C.: Zoom-invariant vision of figural shape: effects on cores of image disturbances. Computer Vision and Image Understanding 69, 72–86 (1998)CrossRefGoogle Scholar
  5. 5.
    Pizer, S.M., Eberly, D., Morse, B.S., Fritsch, D.S.: Zoom-invariant vision of figural shape: The mathematics of cores. Computer Vision and Image Understanding 69, 55–71 (1998)CrossRefGoogle Scholar
  6. 6.
    Frangi, A.F., Niessen, W.J., Hoogeveen, R.M., van Walsum, T., Viergever, M.A.: Modelbased quantitation of 3D magnetic resonance angiographic images. IEEE Transactions on Medical Imaging 18, 946–956 (1999)CrossRefGoogle Scholar
  7. 7.
    Lorigo, L.M., Faugeras, O., Grimson, W.E.L., Keriven, R., Kikinis, R., Westin, C.F.: Co-dimension 2 geodesic active contours for MRA segmentation. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 126–139. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Vasilevskiy, A., Siddiqi, K.: Flux maximizing geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 1565–1578 (2002)CrossRefGoogle Scholar
  9. 9.
    Aylward, S.R., Bullitt, E.: Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Transactions on Medical Imaging 21, 61–75 (2002)CrossRefGoogle Scholar
  10. 10.
    Aylward, S.R., Pizer, S.M., Bullitt, E., Eberly, D.: Intensity ridge and widths for tubular object segmentation and description. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis 56, 131–138 (1996)CrossRefGoogle Scholar
  11. 11.
    Miller, J.E.: Relative Critical Sets and their Application to Image Analysis. Ph.D. Dissertation, Department of Mathematics, University of North Carolina at Chapel Hill (1998)Google Scholar
  12. 12.
    Westin, C.F., Wigstrom, L., Loock, T., Sjoqvist, L., Kikinis, R., Knutsson, H.: Three dimensional adaptive filtering in magnetic resonance angiography. Journal of Magnetic Resonance Imaging 14, 63–71 (2001)CrossRefGoogle Scholar
  13. 13.
    Witkin, A.P.: Scale-space filtering. In: Proceedings of the Eight International Joint Conference on Artificial Intelligence, pp. 1019–1022 (1983)Google Scholar
  14. 14.
    Eberly, D.: Ridges in image and data analysis. Computational Imaging and Vision Series. Kluwer Academic Publishers, Dordrecht (1996)zbMATHGoogle Scholar
  15. 15.
    Blom, J.: Affine invariant corner detection. Ph.D. Thesis, Utrecht University (1991)Google Scholar
  16. 16.
    Lindeberg, T.: Scale-Space Theory in Computer Vision. Kluwer Academic Publishers, Dordrecht (1994)Google Scholar
  17. 17.
    ter Haar Romeny, B.M.: Front-end vision and multi-scale image analysis. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yonatan Fridman
    • 1
  • Stephen M. Pizer
    • 1
  • Stephen Aylward
    • 1
  • Elizabeth Bullitt
    • 1
  1. 1.Medical Image Display & Analysis GroupUniversity of North CarolinaChapel Hill

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