Segmentation of Thalamic Nuclei from DTI Using Spectral Clustering

  • Ulas Ziyan
  • David Tuch
  • Carl-Fredrik Westin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


Recent work shows that diffusion tensor imaging (DTI) can help resolving thalamic nuclei based on the characteristic fiber orientation of the corticothalamic/thalamocortical striations within each nucleus. In this paper we describe a novel segmentation method based on spectral clustering. We use Markovian relaxation to handle spatial information in a natural way, and we explicitly minimize the normalized cut criteria of the spectral clustering for a better optimization. Using this modified spectral clustering algorithm, we can resolve the organization of the thalamic nuclei into groups and subgroups solely based on the voxel affinity matrix, avoiding the need for explicitly defined cluster centers. The identification of nuclear subdivisions can facilitate localization of functional activation and pathology to individual nuclear subgroups.


Thalamic Nucleus Spectral Cluster Spectral Cluster Algorithm Tensor Volume Expert Label 
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.


  1. 1.
    Behrens, T.E., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A., Boulby, P.A., Barker, G.J., Sillery, E.L., Sheehan, K., Ciccarelli, O., Thompson, A.J., Brady, J.M., Matthews, P.M.: Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6(7), 750–757 (2003)CrossRefGoogle Scholar
  2. 2.
    Wiegell, M.R., Tuch, D.S., Larsson, H.B., Wedeen, V.J.: Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. Neuroimage 19, 391–401 (2003)CrossRefGoogle Scholar
  3. 3.
    Beaulieu, C.: The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed. 15(7-8), 435–455 (2002)CrossRefGoogle Scholar
  4. 4.
    Jonasson, L., Hagmann, P., Richero Wilson, C., Bresson, X., Pollo, C., Meuli, R., Thiran, J.P.: Coupled, region based level sets for segmentation of the thalamus and its subnuclei in DT-MRI. In: ISMRM, vol. 731 (2005)Google Scholar
  5. 5.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22(8), 888–905 (2000)Google Scholar
  6. 6.
    Higham, D., Kibble, M.: A unified view of spectral clustering. Mathematics Research Report, 1–17 (2004)Google Scholar
  7. 7.
    Kannan, R., Vempala, S., Vetta, A.: On clusterings: Good, bad and spectral. J ACM 51(3), 497–515 (2004)CrossRefMathSciNetMATHGoogle Scholar
  8. 8.
    Weiss, Y.: Segmentation using eigenvectors: A unifying view. In: ICCV, vol. 975 (1999)Google Scholar
  9. 9.
    Jones, D.K., Horsfield, M.A., Simmons, A.: Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. MRM 42(3), 515–525 (1999)Google Scholar
  10. 10.
    Alexander, D.C., Gee, J.C., Bajcsy, R.: Similarity measures for matching diffusion tensor images. In: BMVC (1999)Google Scholar
  11. 11.
    Wang, Z., Vemuri, B.C.: An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation. CVPR (1), 228–233 (2004)Google Scholar
  12. 12.
    Tishby, N., Slonim, N.: Data clustering by markovian relaxation and the information bottleneck method. In: NIPS, pp. 640–646 (2000)Google Scholar
  13. 13.
    Reese, T.G., Heid, O., Weisskoff, R.M., Wedeen, V.J.: Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo. MRM 49(1), 177–182 (2003)Google Scholar
  14. 14.
    Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2), 825–841 (2002)CrossRefGoogle Scholar
  15. 15.
    Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103(3), 247–254 (1994)CrossRefGoogle Scholar
  16. 16.
    Pierpaoli, C., Basser, P.J.: Toward a quantitative assessment of diffusion anisotropy. MRM 36(6), 893–906 (1996)Google Scholar
  17. 17.
    Mazziotta, J.C., Toga, A.W., Evans, A., Fox, P., Lancaster, J.: A probabilistic atlas of the human brain: theory and rationale for its development. The international consortium for brain mapping (icbm). Neuroimage 2(2), 89–101 (1995)Google Scholar
  18. 18.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ulas Ziyan
    • 1
  • David Tuch
    • 2
  • Carl-Fredrik Westin
    • 1
    • 3
  1. 1.MIT Computer Science and Artificial Intelligence LabCambridgeUSA
  2. 2.Novartis Pharma AGBaselSwitzerland
  3. 3.Laboratory of Mathematics in ImagingBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

Personalised recommendations