A Learning Based Approach for 3D Segmentation and Colon Detagging

  • Zhuowen Tu
  • Xiang (Sean) Zhou
  • Dorin Comaniciu
  • Luca Bogoni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)


Foreground and background segmentation is a typical problem in computer vision and medical imaging. In this paper, we propose a new learning based approach for 3D segmentation, and we show its application on colon detagging. In many problems in vision, both the foreground and the background observe large intra-class variation and inter-class similarity. This makes the task of modeling and segregation of the foreground and the background very hard. The framework presented in this paper has the following key components: (1) We adopt probabilistic boosting tree [9] for learning discriminative models for the appearance of complex foreground and background. The discriminative model ratio is proved to be a pseudo-likelihood ratio modeling the appearances. (2) Integral volume and a set of 3D Haar filters are used to achieve efficient computation. (3) We devise a 3D topology representation, grid-line, to perform fast boundary evolution. The proposed algorithm has been tested on over 100 volumes of size 500 × 512 × 512 at the speed of 2 ~ 3 minutes per volume. The results obtained are encouraging.


Grid Node Discriminative Model Input Volume Virtual Colonoscopy Boundary Evolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhuowen Tu
    • 1
  • Xiang (Sean) Zhou
    • 2
  • Dorin Comaniciu
    • 1
  • Luca Bogoni
    • 2
  1. 1.Integrated Data Systems DepartmentSiemens Corporate ResearchPrincetonUSA
  2. 2.CAD SolutionsSiemens Medical SolutionsMalvernUSA

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