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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)

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.

Keywords

Multi-atlas segmentation Majority voting Label fusion 

Notes

Acknowledgment

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

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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

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