Segmentation of Liver Tumor Using Efficient Global Optimal Tree Metrics Graph Cuts

  • Ruogu Fang
  • Ramin Zabih
  • Ashish Raj
  • Tsuhan Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7029)

Abstract

We propose a novel approach that applies global optimal tree-metrics graph cuts algorithm on multi-phase contrast enhanced contrast enhanced MRI for liver tumor segmentation. To address the difficulties caused by low contrasted boundaries and high variability in liver tumor segmentation, we first extract a set of features in multi-phase contrast enhanced MRI data and use color-space mapping to reveal spatial-temporal information invisible in MRI intensity images. Then we apply efficient tree-metrics graph cut algorithm on multi-phase contrast enhanced MRI data to obtain global optimal labeling in an unsupervised framework. Finally we use tree-pruning method to reduce the number of available labels for liver tumor segmentation. Experiments on real-world clinical data show encouraging results. This approach can be applied to various medical imaging modalities and organs.

Keywords

multi-phase contrast enhanced MRI tree-metrics graph cuts liver tumor segmentation color-space mapping global optimal labeling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ruogu Fang
    • 1
  • Ramin Zabih
    • 2
  • Ashish Raj
    • 3
  • Tsuhan Chen
    • 1
  1. 1.Department of Electrical and Computer EngineeringCornell UniversityIthacaUSA
  2. 2.Department of Computer ScienceCornell UniversityIthacaUSA
  3. 3.Department of RadiologyCornell UniversityNew York CityUSA

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