Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
Wolz, R., et al.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans. Med. Imag. 32(9), 1723–1730 (2013)
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)
Wu, G., et al.: Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. NeroImage 103, 34–46 (2015)
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)
Asman, A.J., Landman, B.A.: Non-local statistical label fusion for multi-atlas segmentation. Med. Image Anal. 17(2), 194–208 (2013)
Sabuncu, M.R., et al.: A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imag. 29(10), 1714–1729 (2010)
Zijdenbos, A.P., et al.: Morphometric analysis of whitematter lesions in MR images: method and validation. IEEE Trans. Med. Imag. 13, 716–724 (1994)
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)
Acknowledgment
The work is partly supported by the NSERC, Kansas NASA EPSCoR Program, and the NSFC (61273282).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Huo, J., Wang, G., Wu, Q.M.J., Thangarajah, A. (2015). Label Fusion for Multi-atlas Segmentation Based on Majority Voting. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-20801-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20800-8
Online ISBN: 978-3-319-20801-5
eBook Packages: Computer ScienceComputer Science (R0)