Advertisement

Tensor-Based Graph-Cut in Riemannian Metric Space and Its Application to Renal Artery Segmentation

  • Chenglong WangEmail author
  • Masahiro Oda
  • Yuichiro Hayashi
  • Yasushi Yoshino
  • Tokunori Yamamoto
  • Alejandro F. Frangi
  • Kensaku Mori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Renal artery segmentation remained a big challenging due to its low contrast. In this paper, we present a novel graph-cut method using tensor-based distance metric for blood vessel segmentation in scale-valued images. Conventional graph-cut methods only use intensity information, which may result in failing in segmentation of small blood vessels. To overcome this drawback, this paper introduces local geometric structure information represented as tensors to find a better solution than conventional graph-cut. A Riemannian metric is utilized to calculate tensors statistics. These statistics are used in a Gaussian Mixture Model to estimate the probability distribution of the foreground and background regions. The experimental results showed that the proposed graph-cut method can segment about \(80\,\%\) of renal arteries with 1mm precision in diameter.

Keywords

Blood vessel segmentation Graph-cut Renal artery Tensor Hessian matrix Riemannian manifold 

Notes

Acknowledgments

Parts of this research were supported by MEXT and JSPS KAKENHI (26108006, 26560255, 25242047), Kayamori Foundation and the JSPS Bilateral International Collaboration Grants.

References

  1. 1.
    Sato, Y., et al.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med. IA 2(2), 143–168 (1998)Google Scholar
  2. 2.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). doi: 10.1007/BFb0056195CrossRefGoogle Scholar
  3. 3.
    Friman, O., et al.: Multiple hypothesis template tracking of small 3D vessel structures. Med. Image Anal. 14(2), 160–171 (2010)CrossRefGoogle Scholar
  4. 4.
    Skibbe, H., et al.: Efficient monte carlo image analysis for the location of vascular entity. IEEE Trans. Med. Imaging 34(2), 628–643 (2015)CrossRefGoogle Scholar
  5. 5.
    Wang, C., et al.: Precise renal artery segmentation for estimation of renal vascular dominant regions. In: SPIE Medical Imaging, pp. 97842M–97842M. International Society for Optics and Photonics (2016)Google Scholar
  6. 6.
    Han, S., Wang, X.: Texture segmentation using graph cuts in spectral decomposition based Riemannian multi-scale nonlinear structure tensor space. Int. J. Comput. Theory Eng. 7(4), 259 (2015)Google Scholar
  7. 7.
    Barmpoutis, A., et al.: Tensor splines for interpolation and approximation of DT-MRI with applications to segmentation of isolated rat hippocampi. IEEE Trans. Med. Imaging 26(11), 1537–1546 (2007)CrossRefGoogle Scholar
  8. 8.
    Fletcher, P.T., Joshi, S.: Riemannian geometry for the statistical analysis of diffusion tensor data. Signal Process. 87(2), 250–262 (2007)CrossRefGoogle Scholar
  9. 9.
    Pennec, X., Fillard, P., Ayache, N.: A Riemannian framework for tensor computing. Int. J. Comput. Vis. 66(1), 41–66 (2006)CrossRefGoogle Scholar
  10. 10.
    Kendall, W.S.: Probability, convexity, and harmonic maps with small image I: uniqueness and fine existence. Proc. Lond. Math. Soc. 3(2), 371–406 (1990)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Barachant, A., et al.: Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing 112, 172–178 (2013)CrossRefGoogle Scholar
  12. 12.
    Boykov, Y., et al.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Chenglong Wang
    • 1
    Email author
  • Masahiro Oda
    • 1
  • Yuichiro Hayashi
    • 2
  • Yasushi Yoshino
    • 3
  • Tokunori Yamamoto
    • 3
  • Alejandro F. Frangi
    • 4
  • Kensaku Mori
    • 1
    • 2
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan
  2. 2.Information and Communications HeadquartersNagoya UniversityNagoyaJapan
  3. 3.Graduate School of MedicineNagoya UniversityNagoyaJapan
  4. 4.Electronic and Electrical Engineering DepartmentUniversity of SheffieldSheffieldUnited Kingdom

Personalised recommendations