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Feature Learning Based Random Walk for Liver Segmentation

Author

Listed:
  • Yongchang Zheng
  • Danni Ai
  • Pan Zhang
  • Yefei Gao
  • Likun Xia
  • Shunda Du
  • Xinting Sang
  • Jian Yang

Abstract

Liver segmentation is a significant processing technique for computer-assisted diagnosis. This method has attracted considerable attention and achieved effective result. However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between the liver and adjacent organs. This paper proposes a feature-learning-based random walk method for liver segmentation using CT images. Four texture features were extracted and then classified to determine the classification probability corresponding to the test images. Seed points on the original test image were automatically selected and further used in the random walk (RW) algorithm to achieve comparable results to previous segmentation methods.

Suggested Citation

  • Yongchang Zheng & Danni Ai & Pan Zhang & Yefei Gao & Likun Xia & Shunda Du & Xinting Sang & Jian Yang, 2016. "Feature Learning Based Random Walk for Liver Segmentation," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-17, November.
    • Handle: RePEc:plo:pone00:0164098
    DOI: 10.1371/journal.pone.0164098
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