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A Robust Energy Minimization Algorithm for MS-Lesion Segmentation

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

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Abstract

The detection of multiple sclerosis lesion is important for many neuroimaging studies. In this paper, a new automatic robust algorithm for lesion segmentation based on MR images is proposed. This method takes full advantage of the decomposition of MR images into the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity. An energy function is defined in term of the property of true image and bias field. The energy minimization is proposed for seeking the optimal segmentation result of lesions and white matter. Then postprocessing operations is used to select the most plausible lesions in the obtained hyperintense signals. The experimental results show that our approach is effective and robust for the lesion segmentation.

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Acknowledgment

This research was partly supported by National Natural Science Foundation of China(NSFC) under Grant No. 61302012 and No. 61172002, the Fundamental Research Funds for the Central Universities under Grant N130418002 and N120518001, and Liaoning Natural Science Foundation under Grant No. 2013020021.

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Authors and Affiliations

  1. Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning, 110819, China

    Zhaoxuan Gong, Dazhe Zhao & Wenjun Tan

  2. Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia, 19104, USA

    Chunming Li & Christos Davatzikos

Authors
  1. Zhaoxuan Gong
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  2. Dazhe Zhao
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  3. Chunming Li
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  4. Wenjun Tan
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  5. Christos Davatzikos
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Corresponding author

Correspondence to Dazhe Zhao .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Gong, Z., Zhao, D., Li, C., Tan, W., Davatzikos, C. (2015). A Robust Energy Minimization Algorithm for MS-Lesion Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_47

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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