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BioMed Research International
Volume 2013 (2013), Article ID 701514, 8 pages
Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer
Key Laboratory for Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Received 29 May 2013; Revised 13 September 2013; Accepted 20 October 2013
Academic Editor: Chung-Chi Lee
Copyright © 2013 Yuhan Yang 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.
- R. Siegel, D. Naishadham, and A. Jemal, “Cancer statistics,” CA: A Cancer Journal for Clinicians, vol. 63, no. 1, pp. 11–30, 2013.
- J. Staffurth, “A review of the clinical evidence for intensity-modulated radiotherapy,” Clinical Oncology, vol. 22, no. 8, pp. 643–657, 2010.
- R. A. Weersink, J. Qiu, A. J. Hope et al., “Improving superficial target delineation in radiation therapy with endoscopic tracking and registration,” Medical Physics, vol. 38, no. 12, pp. 6458–6468, 2011.
- C. F. Njeh, “Tumor delineation: the weakest link in the search for accuracy in radiotherapy,” Journal of Medical Physics, vol. 33, no. 4, pp. 136–140, 2008.
- T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models-their training and application,” Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38–59, 1995.
- T. F. Cooles, G. J. Edwards, and C. J. Taylor, “Active appearance models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 681–685, 2001.
- M. S. Brown, M. F. McNitt-Gray, N. J. Mankovich et al., “Method for segmenting chest CT image data using an anatomical model: preliminary results,” IEEE Transactions on Medical Imaging, vol. 16, no. 6, pp. 828–839, 1997.
- L. Zhang, E. A. Hoffman, and J. M. Reinhardt, “Atlas-driven lung lobe segmentation in volumetric X-ray CT images,” IEEE Transactions on Medical Imaging, vol. 25, no. 1, pp. 1–16, 2006.
- A. A. Qazi, V. Pekar, J. Kim, J. Xie, S. L. Breen, and D. A. Jaffray, “Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach,” Medical Physics, vol. 38, no. 11, pp. 6160–6170, 2011.
- D. L. Collins, C. J. Holmes, T. M. Peters, and A. C. Evans, “Automatic 3-D model-based neuroanatomical segmentation,” Human Brain Mapping, vol. 3, no. 3, pp. 190–208, 1995.
- D. Shen and C. Davatzikos, “HAMMER: hierarchical attribute matching mechanism for elastic registration,” IEEE Transactions on Medical Imaging, vol. 21, no. 11, pp. 1421–1439, 2002.
- H. Bay, T. Tuytelaars, and L. Van Gool, Surf Speeded Up Robust Features, Computer Vision-ECCV, Springer, 2006.
- F. L. Bookstein, “Principal warps: thin-plate splines and the decomposition of deformations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 6, pp. 567–585, 1989.
- Y. Xie, M. Chao, P. Lee, and L. Xing, “Feature-based rectal contour propagation from planning CT to cone beam CT,” Medical Physics, vol. 35, no. 10, pp. 4450–4459, 2008.
- D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
- Y. H. Yang and Y. Q. Xie, “Feature-based GDLOH deformable registration for CT lung image,” Applied Mechanics and Materials, vol. 333, pp. 969–973, 2013.
- J. Duchon, Splines Minimizing Rotation-Invariant Semi-Norms in Sobolev Spaces[M]//Constructive Theory of Functions of Several Variables, Springer, Berlin, Germany, 1977.
- Y. Xie, M. Chao, and G. Xiong, “Deformable image registration of liver with consideration of lung sliding motion,” Medical Physics, vol. 38, no. 10, pp. 5351–5361, 2011.
- L. Ibanez, W. Schroeder, L. Ng, et al., The ITK software guide, 2003.
- G. Bradski and A. Kaehler, “Learning OpenCV: Computer vision with the OpenCV library,” O'Reilly Media, Incorporated, 2008.
- VOLVIEW, http://www.kitware.com/opensource/volview.html.
- PARAVIEW, http://www.paraview.org/.
- T. V. Toolkit, http://www.vtk.org/.
- L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, no. 3, pp. 297–302, 1945.
- K. Van Leemput, F. Maes, D. Vandermeulen, et al., “Automated model-based tissue classification of MR images of the brain,” IEEE Transactions on Medical Imaging, vol. 18, no. 10, pp. 897–908, 1999.
- D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850–863, 1993.
- J. Lian, L. Xing, S. Hunjan, et al., “. Mapping of the prostate in endorectal coil-based MRI/MRSI and CT: a deformable registration and validation study,” Medical Physics, vol. 31, no. 11, pp. 3087–3094, 2004.
- A. Roy and P. Wells, “Volume definition in radiotherapy planning for lung cancer: how the radiologist can help,” Cancer Imaging, vol. 6, no. 1, article 116, 2006.
- J. Mille, “Narrow band region-based active contours and surfaces for 2D and 3D segmentation,” Computer Vision and Image Understanding, vol. 113, no. 9, pp. 946–965, 2009.
- W. Crum, T. Hartkens, and D. Hill, “Non-rigid image registration: theory and practice,” British Journal of Radiology, vol. 77, supplement 2, pp. SS140–SS53, 2004.