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Automatic Localization and Quantification of Intracranial Aneurysms

  • Sahar Hassan
  • Franck Hétroy
  • François Faure
  • Olivier Palombi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6854)

Abstract

We discuss in this paper the problem of localizing and quantifying intracranial aneurysms. Assuming that the segmentation of medical images is done, and that a 3D representation of the vascular tree is available, we present a new automatic algorithm to extract vessels centerlines. Aneurysms are then automatically detected by studying variations of vessels diameters. Once an aneurysm is detected, we give measures that are important to decide its treatment. The name of the aneurysm-carrying vessel is computed using an inexact graph matching technique. The proposed approach is evaluated on segmented real images issued from Magnetic Resonance Angiography (MRA) and CT scan.

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References

  1. 1.
    Ujiie, H., Tachibana, H., Hiramatsu, O., Hazel, A., Matsumoto, T., Ogasawara, Y., Nakajima, H., Hori, T., Takakura, K., Kajiya, F.: Effectes of size and shape (aspect ratio) on the heomdynamics of saccular aneurysms: a possible index for surgical treatment of intracranial aneurysms. Neurosurgery 45, 119–130 (1999)Google Scholar
  2. 2.
    Weir, B.: Unruptured intracranial aneurysms: a review. J. Neurosurgery 96, 3–42 (2002)CrossRefGoogle Scholar
  3. 3.
    Ecker, R., Hopkins, L.: Natural history of unruptured intracranial aneurysms. Neurosurg Focus 17(5) (2004)Google Scholar
  4. 4.
    Wilson, D.L., Noble, J.A.: Segmentation of cerebral vessels and aneurysms from mr angiography data. In: Duncan, J.S., Gindi, G. (eds.) IPMI 1997. LNCS, vol. 1230, pp. 423–428. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  5. 5.
    Aylward, S., Pizer, S., Eberly, D., Bullitt, E.: Intensity ridge and widths for tubular object segmentation and description. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, p. 0131 (1996)Google Scholar
  6. 6.
    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
  7. 7.
    Wink, O., Niessen, W., Viergever, M.: Multiscale vessel tracking. Medical Image Analysis 23(1), 130–133 (2004)CrossRefGoogle Scholar
  8. 8.
    Descoteaux, M., Collins, D.L., Siddiqi, K.: A geometric flow for segmenting vasculature in proton-density weighted mri. Medical Image Analysis 12(4), 497–513 (2008)CrossRefGoogle Scholar
  9. 9.
    Millán, R.D., Dempere-Marco, L., Pozo, J., Cebral, J., Frangi, A.: Morphological characterization of intracranial aneurysms using 3-d moment invariants. IEEE Transactions on Medical Imaging 26(9), 1270–1282 (2007)CrossRefGoogle Scholar
  10. 10.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    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(2), 61–75 (2002)CrossRefGoogle Scholar
  12. 12.
    Deschamps, T., Cohen, L.: Fast extraction of minimal paths in 3D images and applications to virtual endoscopy. Medical Image Analysis 5(4) (2001)Google Scholar
  13. 13.
    Cornea, N., Silver, D., Min, P.: Curve-skeleton properties, applications, and algorithms. IEEE Transactions on Visualization and Computer Graphics 13(3), 530–548 (2007)CrossRefGoogle Scholar
  14. 14.
    Bitter, I., Sato, M., Bender, M., McDonnell, K.T., Kaufman, A., Wan, M.: CEASAR: a smooth, accurate and robust centerline extraction algorithm. In: VIS 2000: Proceedings of the Conference on Visualization 2000, pp. 45–52. IEEE Computer Society Press, Los Alamitos (2000)CrossRefGoogle Scholar
  15. 15.
    Bitter, I., Kaufman, A.E., Sato, M.: Penalized-distance volumetric skeleton algorithm. IEEE Transactions on Visualization and Computer Graphics 7(3), 195–206 (2001)CrossRefGoogle Scholar
  16. 16.
    Wan, M., Liang, Z., Ke, Q., Hong, L., Bitter, I., Kaufman, A.E.: Automatic centerline extraction for virtual colonoscopy. IEEE Trans. Med. Imaging 21, 1450–1460 (2002)CrossRefGoogle Scholar
  17. 17.
    Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: An improved algorithm for matching large graphs. In: 3rd IAPRTC15 Workshop on Graph-based representations in Pattern Recognition, pp. 149–159 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sahar Hassan
    • 1
    • 2
  • Franck Hétroy
    • 1
    • 2
  • François Faure
    • 1
    • 2
  • Olivier Palombi
    • 1
    • 2
    • 3
  1. 1.Laboratoire Jean KuntzmannUniversité de Grenoble & CNRSGrenobleFrance
  2. 2.INRIA Grenoble - Rhône-AlpesGrenobleFrance
  3. 3.Grenoble University HospitalFrance

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