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International Journal of Biomedical Imaging
Volume 2007, Article ID 26950, 15 pages
http://dx.doi.org/10.1155/2007/26950
Research Article

Level Set Method for Positron Emission Tomography

1Department of Mathematics, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1555, USA
2Center for Integrated Petroleum Research, University of Bergen, CIPR room 4103, Allégaten 41, Bergen 5007, Norway
3Department of Scientific Computing, Simula Research Laboratory AS, Lysaker 1325, Norway
4Department of Mathematics and System Sciences, Henan University, Kaifeng 475001, China
5Department of Mathematics, University of Bergen, Johannes Brunsgate 12, Bergen 5009, Norway

Received 26 December 2006; Accepted 6 May 2007

Academic Editor: Hongkai Zhao

Copyright © 2007 Tony F. Chan 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.

Citations to this Article [10 citations]

The following is the list of published articles that have cited the current article.

  • Oliver Dorn, and Dominique Lesselier, “Level set methods for inverse scattering-some recent developments,” Inverse Problems, vol. 25, no. 12, 2009. View at Publisher · View at Google Scholar
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  • Musa Alrefaya, Hichem Sahli, Iris Vanhamel, and Dinh Nho Hao, “A nonlinear probabilistic curvature motion filter for positron emission tomography images,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5567, pp. 212–223, 2009. View at Publisher · View at Google Scholar
  • Heyu Huang, Xiaochao Qu, Jimin Liang, Xiaowei He, Xueli Chen, Da'an Yang, and Jie Tian, “A multi-phase level set framework for source reconstruction in bioluminescence tomography,” Journal Of Computational Physics, vol. 229, no. 13, pp. 5246–5256, 2010. View at Publisher · View at Google Scholar
  • Florin Neacsu, Rostyslav Boutchko, Archontis Giannakidis, and Grant T. Gullberg, “A level set approach to segmenting a deforming myocardium from dynamically acquired spect projection data,” IEEE Nuclear Science Symposium Conference Record, pp. 3588–3592, 2010. View at Publisher · View at Google Scholar
  • Sungwon Yoon, Angel R. Pineda, and Rebecca Fahrig, “Simultaneous segmentation and reconstruction: A level set method approach for limited view computed tomography,” Medical Physics, vol. 37, no. 5, pp. 2329, 2010. View at Publisher · View at Google Scholar
  • Ming Hao, Wenzhong Shi, Hua Zhang, and Chang Li, “Unsupervised change detection with expectation-maximization-based level set,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 1, pp. 210–214, 2014. View at Publisher · View at Google Scholar
  • Oliver Dorn, and Dominique Lesselierpp. 471–532, 2015. View at Publisher · View at Google Scholar
  • Vedrana Andersen Dahl, Anders Bjorholm Dahl, and Per Christian Hansen, “Computing segmentations directly from x-ray projection data via parametric deformable curves,” Measurement Science and Technology, vol. 29, no. 1, pp. 014003, 2017. View at Publisher · View at Google Scholar
  • Kévin Gosse, François Lecellier, Stéphanie Jehan-Besson, and Su Ruan, “Comparison of 2D and 3D region-based deformable models and random walker methods for PET segmentation,” 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, 2017. View at Publisher · View at Google Scholar