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A Deformable Vessel Model with Single Point Initialization for Segmentation, Quantification, and Visualization of Blood Vessels in 3D MRA

  • Marcela Hernández-Hoyos
  • Alfred Anwander
  • Maciej Orkisz
  • Jean-Pierre Roux
  • Philippe Douek
  • Isabelle E. Magnin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1935)

Abstract

We deal with image segmentation applied to three-dimensional (3D) analysis of of vascular morphology in magnetic resonance angiography (MRA) images. The main goal of our work is to develop a fast and reliable method for stenosis quantification. The first step towards this purpose is the extraction of the vessel axis by an expansible skeleton method. Vessel boundaries are then detected in the planes locally orthogonal to the centerline using an improved active contour. Finally, area measurements based on the resulting contours allow the calculation of stenosis parameters. The expansible nature of the skeleton associated with a single point initialization of the active contour allows overcoming some limitations of traditional deformable models. As a result, the algorithm performs well even for severe stenosis and significant vessel curvatures. Experimental results are presented in 3D phantom images as well as in real images of patients.

References

  1. 1.
    Hu, X., Alperin, N., Levin, D., Tan, K., Mengeot, M.: Visualization of MR angiographic data with segmentation and volume-rendering techniques. J. Magnetic Resonance Imaging 1, 539–546 (1991)CrossRefGoogle Scholar
  2. 2.
    Wilson, D.L., Noble, J.A.: Segmentation of cerebral vessels and aneurysms from MR angiography data. In: Information Processing in Med Imaging. 15th Int. Conf., pp. 423–428. Springer, Heidelberg (1997)Google Scholar
  3. 3.
    Masutani, Y., Schiemann, T., Höne, K.-H.: Vascular shape segmentation and structure extraction using a shape-based region-growing model. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1242–1249. Springer, Heidelberg (1998)Google Scholar
  4. 4.
    Chung, A.C.S., Noble, J.A.: Statistical 3D vessel segmentation using a Rician distribution. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 82–89. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  5. 5.
    Verdonck, B., Bloch, I., Maître, H.: Accurate segmentation of blood vessels from 3D medical images. In: ICIP 1996, Lausanne, pp. 311–314 (1996)Google Scholar
  6. 6.
    Nazarian, B., Chédot, C., Sequeira, J.: Automatic reconstruction of irregular tubular structures using generalized cylinders. Revue de CFAO et d’informatique graphique 11(1-2), 11–20 (1996)Google Scholar
  7. 7.
    Lorenz, C., Carlsen, I.-C., Buzug, T.M., Fassnacht, C., Weese, J.: Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 233–242. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  8. 8.
    Wink, O., Niessen, W.J., Viergever, M.A.: Fast quantification of abdominal aorta aneurysmes from CTA volumes. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 138–145. Springer, Heidelberg (1998)Google Scholar
  9. 9.
    Wang, K., Dutton, R.W., Taylor, C.A.: Improving geometric model construction for blood flow modeling. IEEE Engineering in Medicine and Biology 18(6), 33–39 (1999)CrossRefGoogle Scholar
  10. 10.
    Frangi, A.F., Niessen, W.J., et al.: Model-Based quantitation of 3-D magnetic resonance angiographic images. IEEE Transactions on Medical Imaging 18(10), 946–956 (1999)CrossRefGoogle Scholar
  11. 11.
    Bulpitt, A., Berry, E.: An automatic 3D deformable model for segmentation of branching structures compared with interactive region growing. In: Med. Image Understanding Anal., Leeds UK, pp. 25–28 (1998)Google Scholar
  12. 12.
    Shani, U., Ballard, D.: Splines as embeddings fot generalized cylinders. In: CVGIP 1984, vol. 27(2) (1984)Google Scholar
  13. 13.
    Hernández-Hoyos, M., Orkisz, M., et al.: Inertia-based vessel axis extraction and stenosis quantification in 3D MRA images. In: CARS 1999, Paris, pp. 189–193 (1999)Google Scholar
  14. 14.
    Kass, M., Witkin, A., Terzopoulos, D.: Active contour models. Int. J. Computer Vision 1, 321–331 (1988)Google Scholar
  15. 15.
    Cohen, L.D.: On Active Contour Models and Balloons. Computer Vision, Graphics and Image Processing: Image Understanding 53(2), 211–218 (1991)zbMATHGoogle Scholar
  16. 16.
    Sato, Y., Nakajima, S., et al.: 3D Multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 213–222. Springer, Heidelberg (1997)Google Scholar
  17. 17.
    Prinet, V., Monga, O.: Vessels representation in 2D and 3D angiograms. In: CARS 1997, pp. 240–245 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Marcela Hernández-Hoyos
    • 1
  • Alfred Anwander
    • 1
  • Maciej Orkisz
    • 1
  • Jean-Pierre Roux
    • 1
  • Philippe Douek
    • 1
  • Isabelle E. Magnin
    • 1
  1. 1.CREATISCNRS Research Unit (UMR 5515) affiliated to INSERM, Lyon, France CREATIS, INSA 502Villeurbanne cedexFrance

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