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Journal of Real-Time Image Processing

, Volume 13, Issue 1, pp 121–133 | Cite as

Accelerated liver tumor segmentation in four-phase computed tomography images

  • Faten ChaiebEmail author
  • Tarek Ben Said
  • Sabra Mabrouk
  • Faouzi Ghorbel
Special Issue Paper

Abstract

Segmentation and volume measurement of liver tumor are important tasks for surgical planning and cancer follow-up. In this work, a segmentation method from four-phase computed tomography images is proposed. It is based on the combination of the Expectation-Maximization algorithm and the Hidden Markov Random Fields. The latter considers the spatial information given by voxel neighbors of two contrast phases. The segmentation algorithm is applied on a volume of interest that decreases the number of processed voxels. To accelerate the classification steps within the segmentation process, a Bootstrap resampling scheme is also adopted. It consists in selecting randomly an optimal representative set of voxels. The experimental results carried out on three clinical datasets show the performance of our liver tumor segmentation method. It has been notably observed that the computing time of the classification algorithm is reduced without any significant impact on the segmentation accuracy.

Keywords

Segmentation Liver tumor HMRF-EM Bootstrap resampling Computed tomography 

Notes

Acknowledgments

The authors thank Dr. Olfa Azaiz and Prof. Emna Mnif from the Department of Radiology, La Rabta Hospital, Tunis, Tunisia.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

No need (no personal data are used in this work).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Faten Chaieb
    • 1
    Email author
  • Tarek Ben Said
    • 1
  • Sabra Mabrouk
    • 1
  • Faouzi Ghorbel
    • 1
  1. 1.CRISTAL Laboratory, ENSIUniversity of ManoubaManoubaTunisia

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