Vessel Connectivity Using Murray’s Hypothesis

  • Yifeng Jiang
  • Zhen W. Zhuang
  • Albert J. Sinusas
  • Lawrence H. Staib
  • Xenophon Papademetris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

We describe a new method for vascular image analysis that incorporates a generic physiological principle to estimate vessel connectivity, which is a key issue in reconstructing complete vascular trees from image data. We follow Murray’s hypothesis of the minimum work principle to formulate the problem as an optimization problem. This principle reflects a global property of any vascular network, in contrast to various local geometric properties adopted as constraints previously. We demonstrate the effectiveness of our method using a set of microCT mouse coronary images. It is shown that the performance of our method has a statistically significant improvement over the widely adopted minimum spanning tree methods that rely on local geometric constraints.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zagorchev, L., Oses, P., Zhuang, Z.W., Moodie, K., Mulligan-Kehoe, M., Simons, M., Couffinhal, T.: Micro computed tomography for vascular exploration. Journal of Angiogenesis Research 2(1), 7 (2010)CrossRefGoogle Scholar
  2. 2.
    Lesage, D., Angelini, E., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009)CrossRefGoogle Scholar
  3. 3.
    Szymczak, A., Stillman, A., Tannenbaum, A., Mischaikow, K.: Coronary vessel trees from 3d imagery: a topological approach. Med. Image Anal. 10(4), 548–559 (2006)CrossRefGoogle Scholar
  4. 4.
    Lee, J., Beighley, P., Ritman, E., Smith, N.: Automatic segmentation of 3D micro-CT coronary vascular images. Med. Image Anal. 11(6), 630–647 (2007)CrossRefGoogle Scholar
  5. 5.
    Wischgoll, T., Choy, J., Ritman, E., Kassab, G.: Validation of image-based method for extraction of coronary morphometry. Ann. Biomed. Eng. 36(3), 356–368 (2008)CrossRefGoogle Scholar
  6. 6.
    Bauer, C., Pock, T., Sorantin, E., Bischof, H., Beichel, R.: Segmentation of interwoven 3d tubular tree structures utilizing shape priors and graph cuts. Med. Image Anal. 14(2), 172–184 (2010)CrossRefGoogle Scholar
  7. 7.
    Bullitt, E., Aylward, S., Liu, A., Stone, J., Mukherji, S., Coffey, C., Gerig, G., Pizer, S.: 3D graph description of the intracerebral vasculature from segmented MRA and tests of accuracy by comparison with x-ray angiograms. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 308–321. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Jomier, J., LeDigarcher, V., Aylward, S.: Automatic vascular tree formation using the mahalanobis distance. In: Duncan, J., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 806–812. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Murray, C.: The physiological principle of minimum work. I. The vascular system and the cost of blood volume. Proc. Natl. Acad. Sci. 12(3), 207–214 (1926)CrossRefGoogle Scholar
  10. 10.
    Bruyninckx, P., Loeckx, D., Vandermeulen, D., Suetens, P.: Segmentation of liver portal veins by global optimization. In: Proc. of SPIE, vol. 7624, p. 76241Z (2010)Google Scholar
  11. 11.
    Jiang, Y., Zhuang, Z., Sinusas, A., Papademetris, X.: Vascular tree reconstruction by minimizing a physiological functional cost. In: CVPRW, pp. 178–185. IEEE (2010)Google Scholar
  12. 12.
    Vasko, F., Barbieri, R., Rieksts, B., Reitmeyer, K., Stott, K., et al.: The cable trench problem: combining the shortest path and minimum spanning tree problems. Comput. Oper. Res. 29(5), 441–458 (2002)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yifeng Jiang
    • 1
  • Zhen W. Zhuang
    • 2
  • Albert J. Sinusas
    • 1
    • 2
  • Lawrence H. Staib
    • 1
    • 3
    • 4
  • Xenophon Papademetris
    • 1
    • 3
  1. 1.Department of Diagnostic RadiologyYale UniversityNew HavenUSA
  2. 2.Department of Internal Medicine CardiologyYale UniversityNew HavenUSA
  3. 3.Department of Biomedical EngineeringYale UniversityNew HavenUSA
  4. 4.Department of Electrical EngineeringYale UniversityNew HavenUSA

Personalised recommendations