Multimedia Tools and Applications

, Volume 74, Issue 23, pp 10535–10558 | Cite as

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

  • Wei Huang
  • Jing Li
  • Peng Zhang
  • Min Wan
  • Can Fang
  • Minmin Shen


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.


Tumor localization Low-rank and sparse decomposition Similarity learning Spectral clustering 



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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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Information EngineeringNanchang UniversityNanchangChina
  2. 2.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  3. 3.School of Computer and Information ScienceSouthwestern UniversityChongqingChina
  4. 4.School of Software EngineeringSouth China University of TechnologyGuangzhouChina
  5. 5.INCIDE CenterUniversity of KonstanzKonstanzGermany

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