Label Fusion for Multi-atlas Segmentation Based on Majority Voting

  • Jie Huo
  • Guanghui WangEmail author
  • Q. M. Jonathan Wu
  • Akilan Thangarajah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)


Multi-atlas based segmentation is a popular approach in medical image analysis. Majority voting, as the simplest label fusion method in multi-atlas based segmentation, is a powerful tool for segmentation. In this paper, a novel majority voting-based label fusion algorithm is proposed by introducing a patch-based analysis for automatic segmentation of brain MR images. The proposed approach, by comparing the similarity between patches, avoids the over-segmentation problem of the majority fusion. The approach is successfully applied to the segmentation of hippocampus, and the experimental results demonstrate significant improvement over three state-of-the-art approaches in the literature.


Multi-atlas segmentation Majority voting Label fusion 



The work is partly supported by the NSERC, Kansas NASA EPSCoR Program, and the NSFC (61273282).


  1. 1.
    Wang, H., Sun, J., Pluta, J., Craige, J., Yushkevich, P.: Multiatlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 311–623 (2013)Google Scholar
  2. 2.
    Bai, W., et al.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE Trans. Med. Imag. 32(7), 1302–1315 (2013)CrossRefGoogle Scholar
  3. 3.
    Wolz, R., et al.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans. Med. Imag. 32(9), 1723–1730 (2013)CrossRefGoogle Scholar
  4. 4.
    Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Nonlocal patch-based label fusion for hippocampus segmentation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 129–136. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  5. 5.
    Wu, G., et al.: Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. NeroImage 103, 34–46 (2015)CrossRefGoogle Scholar
  6. 6.
    Asman, A.J., Landman, B.A.: Characterizing spatially varying performance to improve multi-atlas multi-label segmentation. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 85–96. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  7. 7.
    Asman, A.J., Landman, B.A.: Non-local statistical label fusion for multi-atlas segmentation. Med. Image Anal. 17(2), 194–208 (2013)CrossRefGoogle Scholar
  8. 8.
    Sabuncu, M.R., et al.: A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imag. 29(10), 1714–1729 (2010)CrossRefGoogle Scholar
  9. 9.
    Zijdenbos, A.P., et al.: Morphometric analysis of whitematter lesions in MR images: method and validation. IEEE Trans. Med. Imag. 13, 716–724 (1994)CrossRefGoogle Scholar
  10. 10.
    Avants, B., Epstein, C., Grossman, M., Gee, J.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jie Huo
    • 1
  • Guanghui Wang
    • 2
    Email author
  • Q. M. Jonathan Wu
    • 1
  • Akilan Thangarajah
    • 1
  1. 1.Department of ECEUniversity of WindsorWindsorCanada
  2. 2.Department of EECSUniversity of KansasLawrenceUSA

Personalised recommendations