Nonrigid Object Modelling and Visualization for Hepatic Surgery Planning in e-Health

  • Suhuai Luo
  • Jiaming Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7733)


This paper introduces an automatic approach of nonrigid object modelling and visualization for hepatic surgery planning, in particular, for live donor liver transplantation and accurate liver resection for cancer in e-health application. The proposed approach can build a system that supports radiologists in data preparation and gives surgeons precise information for making optimal decisions. It provides 3D representation of liver parenchyma and vasculature, and 3D simulation of patient specific data. The system is realized in four major stages, including registration of multimodal images; segmentation of liver parenchyma; extraction of liver vessels; and modelling and visualization of liver parenchyma and vessels. The approach is unique in that it integrates advanced techniques such as machine learning algorithm with a knowledge base of the organ. The details of these stages are described along with experimental results and discussions of the advantages of the approach over other approaches.


visualization modeling segmentation machine learning surgery planning 


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  1. 1.
    Peters, T., Cleary, K. (eds.): Image-Guided Interventions: Technology and Applications. Springer (2008)Google Scholar
  2. 2.
    Radtke, A., et al.: Computer-assisted surgery planning for complex liver resections: when is it helpful? A single-center experience over an 8-year period. Ann. Surg. 252(5), 876–883 (2010)CrossRefGoogle Scholar
  3. 3.
    Selver, M.A., et al.: Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation. Computers in Biology & Medicine 38(7), 765–784 (2008)CrossRefGoogle Scholar
  4. 4.
    Materka, A., Strzelecki, M.: Texture analysis methods - a review. Technical Report, Technical University of Lodz, Institute of Electronics (1998)Google Scholar
  5. 5.
    Cristianini, N., Shawer-Tatlor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2005) ISBN 0521780195Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Suhuai Luo
    • 1
  • Jiaming Li
    • 2
  1. 1.The University of NewcastleAustralia
  2. 2.The CSIRO ICT CentreAustralia

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