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Automatic Liver Segmentation Using Statistical Prior Models and Free-form Deformation

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Medical Computer Vision: Algorithms for Big Data (MCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8848))

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Abstract

In this paper, an automatic and robust coarse-to-fine liver image segmentation method is proposed. Multiple prior knowledge models are built to implement liver localization and segmentation: voxel-based AdaBoost classifier is trained to localize liver position robustly, shape and appearance models are constructed to fit liver these models to original CT volume. Free-form deformation is incorporated to improve the models’ ability of refining liver boundary. The method was submitted to VISCERAL big data challenge, and had been tested on IBSI 2014 challenge datasets and the result demonstrates that the proposed method is accurate and efficient.

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Acknowledgments

The work was supported by the 12th Five-Year National High-tech R&D Program of China (863 Program) (No.2012AA022305) and Guangdong Provincial Scientific & Technology Project (No.2012A080203013 and No.2012A030400013).

Author information

Authors and Affiliations

  1. Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Xuhui Li, Cheng Huang & Fucang Jia

  2. College of Computer and Communication Engineering, China University of Petroleum, Qingdao, 266580, China

    Xuhui Li & Zongmin Li

  3. Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China

    Chihua Fang & Yingfang Fan

Authors
  1. Xuhui Li
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  2. Cheng Huang
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  3. Fucang Jia
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  4. Zongmin Li
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  5. Chihua Fang
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  6. Yingfang Fan
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Corresponding author

Correspondence to Fucang Jia .

Editor information

Editors and Affiliations

  1. Technische Universität München, München, Germany

    Bjoern Menze

  2. Medical University of Vienna, Vienna, Austria

    Georg Langs

  3. GE Global Research, Niskayuna, New York, USA

    Albert Montillo

  4. Siemens AG, Erlangen, Germany

    Michael Kelm

  5. University of Applied Sciences, Sierre, Switzerland

    Henning MĂĽller

  6. University of North Carolina, Charlotte, USA

    Shaoting Zhang

  7. School of Information Technologies, Biomedical & Multimedia Info. Tech.(BMIT) Research Group, University of Sydney, Sydney, New South Wales, Australia

    Weidong (Tom) Cai

  8. Rutgers University, Piscataway, New Jersey, USA

    Dimitris Metaxas

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© 2014 Springer International Publishing Switzerland

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Li, X., Huang, C., Jia, F., Li, Z., Fang, C., Fan, Y. (2014). Automatic Liver Segmentation Using Statistical Prior Models and Free-form Deformation. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2014. Lecture Notes in Computer Science(), vol 8848. Springer, Cham. https://doi.org/10.1007/978-3-319-13972-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-13972-2_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13971-5

  • Online ISBN: 978-3-319-13972-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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