Advertisement

A New Adaptive Probabilistic Model of Blood Vessels for Segmenting MRA Images

  • Ayman El-Baz
  • Aly A. Farag
  • Georgy Gimel’farb
  • Mohamed A. El-Ghar
  • Tarek Eldiasty
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

A new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar (for normal subjects) and turbulent blood flow (in abnormal cases like anemia or stenosis) and results in a fast algorithm for extracting a 3D cerebrovascular system from the MRA data. Experiments with synthetic and 50 real data sets confirm the high accuracy of the proposed approach.

Keywords

Magnetic Resonance Angiography Blood Velocity Deformable Model Abnormal Case Magnetic Resonance Angiography Technique 
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.
    McInerney, T., Terzopoulos, D.: Medical image segmentation using topologically adaptable surface. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 23–32. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  2. 2.
    Lorigo, L.M., Faugeras, O.D., Grimson, W.E.L., Keriven, R.: Curves: Curve evolution for vessel segmentation. Medical Image Analysis 5, 195–206 (2001)CrossRefGoogle Scholar
  3. 3.
    Wink, O., Niessen, W.J., Viergever, M.A.: Fast delineation and visualization of vessels in 3-D angiographic images. IEEE Trans. Med. Imaging 19, 337–346 (2000)CrossRefGoogle Scholar
  4. 4.
    Deschamps, T., Cohen, L.D.: Fast extraction of tubular and tree 3D surfaces with front propoagation methods. In: Proc. 16th ICPR, pp. 731–734 (2002)Google Scholar
  5. 5.
    Yan, P., Kassin, A.A.: MRA images segmentation with capillary active contour. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 51–58. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Frangi, A.F., Niessen, W.J., Hoogeveen, R.M., van Walsum, T., Viergever, M.V.: Model-based quantitation of 3-D magnetic resonance angiographic images. IEEE Transactions on Medical Imaging 18, 946–956 (1999)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Wilson, D.L., Noble, J.A.: An adaptive segmentation algorithm for time-of-flight MRA data. IEEE Trans. Med. Imaging 18, 938–945 (1999)CrossRefGoogle Scholar
  9. 9.
    Chung, A., Noble, J.A., Summers, P.: Fusing speed and phase information for vascular segmentation of phase contrast MR angiograms. Medical Image Analysis 6, 109–128 (2002)CrossRefGoogle Scholar
  10. 10.
    Caro, C.G., et al.: The Mechanics of the Circulation. Oxford University Press, Oxford (1978)Google Scholar
  11. 11.
    Ganong, W.F.: Review of medical physiology, 15th edn. McGraw-Hill, New York (1991)Google Scholar
  12. 12.
    Farag, A.A., El-Baz, A., Gimel’farb, G.: Precise Segmentation of Multi-modal Images. IEEE Transactions on Image Processing 15(4), 952–968 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ayman El-Baz
    • 1
  • Aly A. Farag
    • 1
  • Georgy Gimel’farb
    • 2
  • Mohamed A. El-Ghar
    • 3
  • Tarek Eldiasty
    • 3
  1. 1.Computer Vision and Image Processing LaboratoryUniversity of LouisvilleLouisville
  2. 2.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand
  3. 3.Urology and Nephrology DepartmentUniversity of MansouraMansouraEgypt

Personalised recommendations