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

  • Authors:

  • Yangqiu Song;Changshui Zhang;Jianguo Lee;Fei Wang;Shiming Xiang;Dan Zhang

  • Affiliations:

  • Tsinghua University, State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, 100084, Bei ...;Tsinghua University, State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, 100084, Bei ...;Tsinghua University, State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, 100084, Bei ...;Tsinghua University, State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, 100084, Bei ...;Tsinghua University, State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, 100084, Bei ...;Tsinghua University, State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, 100084, Bei ...

  • Venue:
  • Pattern Analysis & Applications
  • Year:
  • 2009

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Abstract

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.