Accurate 3D Left-Right Brain Hemisphere Segmentation in MR Images Based on Shape Bottlenecks and Partial Volume Estimation

  • Lu Zhao
  • Jussi Tohka
  • Ulla Ruotsalainen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


Current automatic methods based on mid-sagittal plane to segment left and right human brain hemispheres in 3D magnetic resonance (MR) images simply use a planar surface. However, the two brain hemispheres, in fact, can not be separated by just a simple plane properly. A novel automatic method to segment left and right brain hemispheres in MR images is proposed in this paper, which is based on an extended shape bottlenecks algorithm and a fast and robust partial volume estimation approach. In this method, brain tissues firstly are extracted from the MR image of human head. Then the information potential map is generated, according to which a brain hemisphere mask with the same size of the original image is created. 10 simulated and 5 real T1-weighted MR images were used to evaluate this method, and much more accurate segmentation of human brain hemispheres was achieved comparing with the segmentation with mid-sagittal plane.


Brain asymmetry Mid-sagittal plane Stereotaxic registration 


  1. 1.
    Ardekani, B.A., Kershaw, J., Braun, M., Kanno, I.: Automatic detection of the mid-sagittal plane in 3D brain images. IEEE Transactions on Medical Imaging 16(6), 947–952 (1997)CrossRefGoogle Scholar
  2. 2.
    Brett, M., Johnsrude, I.S., Owen, A.M.: The problem of functional localization in the human brain. Nature Reviews Neuroscience 3(3), 243–249 (2002)CrossRefGoogle Scholar
  3. 3.
    Brummer, M.E.: Hough transform detection of the longitudinal fissure in tomographic head images. IEEE Transactions on Medical Imaging 10, 74–81 (1991)CrossRefGoogle Scholar
  4. 4.
    Collins, D.L., Zijdenbos, A.P., Kollokian, v., Sled, J.G., Kabani, N.J., Holmes, C.J., Evans, A.C.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 17(3), 463–468 (1998)CrossRefGoogle Scholar
  5. 5.
    Crow, T.J.: Schizophrenia as an anomaly of cerebral asymmetry. In: Imaging of the Brian in Psychiatry and Related Fields, pp. 1–17. Springer, Berlin (1993)Google Scholar
  6. 6.
    Davidson, R.J., Hugdahl, K.: Brain Asymmetry. MIT Press, Cambridge (1996)Google Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Chichester (2000)Google Scholar
  8. 8.
    Euvrard, D.: Resolution numerique des equations aux derivees partielles. Masson, Paris (1988)Google Scholar
  9. 9.
    Geschwind, N., Levitsky, W.: Human brain: Left-right asymmetries in temporal speech region. Science 161, 186–187 (1968)CrossRefGoogle Scholar
  10. 10.
    Kovalev, V.A., Kruggel, F., von Cramon, D.Y.: Gender and age effects in structural brain asymmetry as measured by MRI texture analysis. NeuroImage 19(3), 895–905 (2003)CrossRefGoogle Scholar
  11. 11.
    Kruggel, F., von Cramon, D.Y.: Alignment of magnetic-resonance brain datasets with the stereotactical coordinate system. Medical image analysis 3(2), 175–185 (1999)CrossRefGoogle Scholar
  12. 12.
    Kwan, R.-S., Evans, A.C., Pike, G.B.: MRI simulation based evaluation and classifications methods. IEEE Trans. Med. Imaging 18(11), 1085–1097 (1999)CrossRefGoogle Scholar
  13. 13.
    Liu, Y., Collins, R.T., Rothfus, W.E.: Automatic bilateral symmetry (midsagittal) plane extraction from pathological 3D neuroradiological images. In: SPIE International Symposium on Medical Imaging. Proceedings of SPIE, vol. 3338 (1998)Google Scholar
  14. 14.
    Liu, Y., Collins, R.T., Rothfus, W.E.: Robust midsagittal plane extraction from normal and pathological 3D neuroradiology images. IEEE Transactions on Medical Imaging 20(3), 175–192 (2001)CrossRefGoogle Scholar
  15. 15.
    Mangin, J.-F., Régis, J., Frouin, V.: Shape bottlenecks and conservative flow systems. In: IEEEWork, MMBIA, San Francisco, CA, pp. 319–328 (1996)Google Scholar
  16. 16.
    Marais, P., Guillemaud, R., Sakuma, M., Zisserman, A., Brady, M.: Visualising cerebral asymmetry. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131, pp. 411–416. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  17. 17.
    Prima, S., Ourselin, S., Ayache, N.: Computation of the mid-sagittal plane in 3D brain images. IEEE Transactions on Medical Imaging 21(2), 122–138 (2002)CrossRefGoogle Scholar
  18. 18.
    Shattuck, D.W., Sandor-Leahy, S.R., Schaper, K.A., Rottenberg, D.A., Leahy, R.M.: Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13(5), 856–876 (2001)CrossRefGoogle Scholar
  19. 19.
    Smith, S., Jenkinson, M.: Accurate Robust Symmetry Estimation. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 308–317. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  20. 20.
    Sun, C., Sherrah, J.: 3D symmetry detection using the extended Gaussian image. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 164–168 (1997)CrossRefGoogle Scholar
  21. 21.
    Toga, W., Thompson, P.M.: Mapping brain asymmetry. Nature Reviews Neuroscience 4(1), 37–48 (2003)CrossRefGoogle Scholar
  22. 22.
    Tohka, J., Zijdenbos, A., Evans, A.C.: Fast and robust parameter estimation for statistical partial volume models in brain MRI. NeuroImage 23(1), 84–97 (2004)CrossRefGoogle Scholar
  23. 23.
    Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macrosopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1), 273–289 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Lu Zhao
    • 1
  • Jussi Tohka
    • 1
  • Ulla Ruotsalainen
    • 1
  1. 1.Institute of Signal Processing, Tampere University of Technology, P.O.Box 553, FIN-33101Finland

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