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BioMed Research International
Volume 2016, Article ID 1480423, 13 pages
http://dx.doi.org/10.1155/2016/1480423
Research Article

Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images

1School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2Xianyang Hospital, Yan’an University, Xianyang 712000, China
3First Affiliated Hospital of School of Medicine, Xian Jiaotong University, Xian 710061, China
4School of Information Science and Technology, Aichi Prefectural University, Nagakute, Aichi 480-1198, Japan
5Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL 60616-3793, USA

Received 28 April 2016; Accepted 19 July 2016

Academic Editor: Weidong Cai

Copyright © 2016 Zhenghao Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. Doel, D. J. Gavaghan, and V. Grau, “Review of automatic pulmonary lobe segmentation methods from CT,” Computerized Medical Imaging and Graphics, vol. 40, pp. 13–29, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, “Random walk and graph cut for co-segmentation of lung tumor on PET-CT-images,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5854–5867, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  3. S. S. Mokri, M. I. Saripan, M. H. Marhaban, and A. J. Nordin, “Lung segmentation in CT for thoracic PET-CT registration through visual study,” in Proceedings of the 2nd IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES '12), pp. 550–554, Langkawi, Malaysia, December 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Iwao, T. Gotoh, S. Kagei, T. Iwasawa, and M. de Sales Guerra Tsuzuki, “Integrated lung field segmentation of injured region with anatomical structure analysis by failure-recovery algorithm from chest CT images,” Biomedical Signal Processing and Control, vol. 12, no. 1, pp. 28–38, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Dai, K. Lu, J. Dong, Y. Zhang, and Y. Chen, “A novel approach of lung segmentation on chest CT images using graph cuts,” Neurocomputing, vol. 168, pp. 799–807, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Mansoor, U. Bagci, Z. Xu et al., “A generic approach to pathological lung segmentation,” IEEE Transactions on Medical Imaging, vol. 33, no. 12, pp. 2293–2310, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Sun, C. Bauer, and R. Beichel, “Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach,” IEEE Transactions on Medical Imaging, vol. 31, no. 2, pp. 449–460, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Sun, C. Bauer, and R. Beichel, “Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach,” IEEE Transactions on Medical Imaging, vol. 31, no. 2, pp. 449–460, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Li, Z. Miao, and B. Wang, “Segmentation of lung CT with pathologies based on adapt active appearance models,” in Proceedings of the 3rd International Conference on Computer Science and Network Technology (ICCSNT '13), pp. 1119–1121, Dalian, China, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. M. R. D. Raj and C. H. Sulochana, “An efficient lung segmentation approach for interstitial lung disease,” in Proceedings of the 2014 International Conference on Circuits, Power and Computing Technologies (ICCPCT '14), pp. 1211–1216, IEEE, Nagercoil, India, March 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Zhou, Y. Cheng, and S. Tamura, “Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels on chest CT images,” Biomedical Signal Processing and Control, vol. 13, no. 1, pp. 62–70, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Zhou, Z. Yan, G. Lasio et al., “Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT,” Computerized Medical Imaging and Graphics, vol. 46, pp. 47–55, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397–1409, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Yim and H. Hong, “Correction of segmented lung boundary for inclusion of pleural nodules and pulmonary vessels in chest CT images,” Computers in Biology and Medicine, vol. 38, no. 8, pp. 845–857, 2008. View at Publisher · View at Google Scholar · View at Scopus