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Abdomen CT Image Segmentation Based on MRF and Ribs Fitting Approach

  • Huiyan Jiang
  • Zhiyuan Ma
  • Mao Zong
  • Hiroshi Fujita
  • Xiangrong Zhou
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 206)

Abstract

Aiming at the segmentation of liver image with fuzzy edge, a new algorithm based on Markov Random Field and ribs fitting approach is proposed. The new algorithm consists of three main steps. Firstly, an abdominal image is pre-processed to fit ribs and remove the obstructive region. Then, lifting wavelet transform is adopted to decompose an image in different resolutions, and an image segmentation algorithm based on MRF is manipulated to the low frequency sub-images; lastly, morphology operation is adopted to obtain the liver region. The algorithms of the initial and multi-level segmentation in wavelet domain are K-means and MAP/ICM. Several experiments have been carried out and the experimental results show that the proposed algorithm has a good robustness and higher segmentation accuracy than the traditional MRF approach.

Keywords

Image segmentation Markov random field Ribs fitting ICM Wavelet transform 

Notes

Acknowledgments

The research is supported by the National Natural Science Foundation of China (No. 50834009 and No. 60973071).

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Huiyan Jiang
    • 1
  • Zhiyuan Ma
    • 1
  • Mao Zong
    • 1
  • Hiroshi Fujita
    • 1
  • Xiangrong Zhou
    • 1
  1. 1.Software CollegeNortheastern UniversityShenyangChina

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