Segmentation of liver vasculature from contrast enhanced CT images using context-based voting

Yanling Chi, Jimin Liu, Sudhakar K. Venkatesh, Su Huang, Jiayin Zhou, Qi Tian, Wieslaw L. Nowinski

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

A novel vessel context-based voting is proposed for automatic liver vasculature segmentation in CT images. It is able to conduct full vessel segmentation and recognition of multiple vasculatures effectively. The vessel context describes context information of a voxel related to vessel properties, such as intensity, saliency, direction, and connectivity. Voxels are grouped to liver vasculatures hierarchically based on vessel context. They are first grouped locally into vessel branches with the advantage of a vessel junction measurement and then grouped globally into vasculatures, which is implemented using a multiple feature point voting mechanism. The proposed method has been evaluated on ten clinical CT datasets. Segmentation of third-order vessel trees from CT images (0.76×0.76 2.0 mm) of the portal venous phase takes less than 3 min on a PC with 2.0 GHz dual core processor and the average segmentation accuracy is up to 98.

Original languageEnglish (US)
Article number5639035
Pages (from-to)2144-2153
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume58
Issue number8
DOIs
StatePublished - Aug 1 2011

Keywords

  • Liver vasculature segmentation
  • multiple feature point voting
  • vessel context
  • vessel junction measure

ASJC Scopus subject areas

  • Biomedical Engineering

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