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A novel marker-less lung tumor localization strategy on low-rank fluoroscopic images with similarity learning

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Abstract

Fluoroscopic images depicting the movement of lung tumor lesions along with patients’ respirations are essential in contemporary image-guided lung cancer radiotherapy, as the accurate delivery of radiation dose on lung tumor lesions can be facilitated with the help of fluoroscopic images. However, the quality of fluoroscopic images is often not high, and several factors including image noise, artifact, ribs occlusion often prevent the tumor lesion from being accurate localized. In this study, a novel marker-less lung tumor localization strategy is proposed. Unlike conventional lung tumor localization strategies, it doesn’t require placing external surrogates on patients or implanting internal fiducial markers in patients. Thus ambiguous movement correlations between moving tumor lesions and surrogates as well as the risk of patients pneumothorax can be totally avoided. In this new strategy, fluoroscopic images are first decomposed into low-rank and sparse components via the split Bregman method, and then spectral clustering techniques are incorporated for similarity learning to realize the tumor localization task. Clinical data obtained from 60 patients with lung tumor lesions is utilized for experimental evaluation, and promising results obtained by the new strategy are demonstrated from the statistical point of view.

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Acknowledgments

This work is supported by 61363046, 61301194, and 61302121 approved by National Natural Science Foundation China, 20142BBE50023, 20142BAB217033 and 20142BAB217030 approved by Jiangxi Provincial Department of Science and Technology, as well as NWPU grant 3102014JSJ0014.

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Authors and Affiliations

  1. School of Information Engineering, Nanchang University, Nanchang, China

    Wei Huang, Jing Li & Min Wan

  2. School of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Peng Zhang

  3. School of Computer and Information Science, Southwestern University, Chongqing, China

    Can Fang

  4. School of Software Engineering, South China University of Technology, Guangzhou, China

    Minmin Shen

  5. INCIDE Center, University of Konstanz, Konstanz, Germany

    Minmin Shen

Authors
  1. Wei Huang
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  2. Jing Li
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  3. Peng Zhang
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  4. Min Wan
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  5. Can Fang
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  6. Minmin Shen
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Correspondence to Wei Huang or Peng Zhang.

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Huang, W., Li, J., Zhang, P. et al. A novel marker-less lung tumor localization strategy on low-rank fluoroscopic images with similarity learning. Multimed Tools Appl 74, 10535–10558 (2015). https://doi.org/10.1007/s11042-014-2186-9

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  • DOI: https://doi.org/10.1007/s11042-014-2186-9

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