Pattern Analysis and Applications

, Volume 12, Issue 2, pp 99–115 | Cite as

Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images

  • Yangqiu SongEmail author
  • Changshui Zhang
  • Jianguo Lee
  • Fei Wang
  • Shiming Xiang
  • Dan Zhang
Theoretical Advances


Due to the large data size of 3D MR brain images and the blurry boundary of the pathological tissues, tumor segmentation work is difficult. This paper introduces a discriminative classification algorithm for semi-automated segmentation of brain tumorous tissues. The classifier uses interactive hints to obtain models to classify normal and tumor tissues. A non-parametric Bayesian Gaussian random field in the semi-supervised mode is implemented. Our approach uses both labeled data and a subset of unlabeled data sampling from 2D/3D images for training the model. Fast algorithm is also developed. Experiments show that our approach produces satisfactory segmentation results comparing to the manually labeled results by experts.


Magnetic resonance imaging (MRI) Brain tumor segmentation Semi-automated segmentation Gaussian random field (GRF) Gaussian process (GP) 



This work is funded by the Basic Research Foundation of Tsinghua National Laboratory for Information Science and Technology (TNList). We would like to thank the anonymous reviewers for their valuable suggestions. We would also like to give special thanks to Qian Wu, Weibei Dou and Yonglei Zhou for providing us their detailed experimental data and code.


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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Yangqiu Song
    • 1
    Email author
  • Changshui Zhang
    • 1
  • Jianguo Lee
    • 1
  • Fei Wang
    • 1
  • Shiming Xiang
    • 1
  • Dan Zhang
    • 1
  1. 1.State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of AutomationTsinghua UniversityBeijingChina

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