Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2014 (2014), Article ID 237648, 8 pages
http://dx.doi.org/10.1155/2014/237648
Research Article

Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation

1College of Information Engineering, Qingdao University, Qingdao 266071, China
2College of Physics Science, Qingdao University, Qingdao 266071, China
3The Affiliated Hospital of Medical College, Qingdao University, Qingdao 266003, China

Received 23 November 2013; Revised 20 January 2014; Accepted 27 January 2014; Published 2 March 2014

Academic Editor: Guowei Wei

Copyright © 2014 Guodong Wang 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. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266–277, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Chen, H. D. Tagare, S. Thiruvenkadam et al., “Using prior shapes in geometric active contours in a variational framework,” International Journal of Computer Vision, vol. 50, no. 3, pp. 315–328, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. S. C. Zhu, “Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884–900, 1996. View at Publisher · View at Google Scholar · View at Scopus
  4. N. Paragios and R. Deriche, “Geodesic active regions: a new framework to deal with frame partition problems in computer vision,” Journal of Visual Communication and Image Representation, vol. 13, no. 1-2, pp. 249–268, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Rousson and R. Deriche, “A variational framework for active and adaptative segmentation of vector valued images,” in Proceedings of the Workshop on Motion and Video Computing, pp. 56–61, 2002.
  6. A. Sarti, C. Corsi, E. Mazzini, and C. Lamberti, “Maximum likelihood segmentation of ultrasound images with rayleigh distribution,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 52, no. 6, pp. 947–960, 2005. View at Google Scholar · View at Scopus
  7. C. Li, C.-Y. Kao, J. C. Gore, and Z. Ding, “Minimization of region-scalable fitting energy for image segmentation,” IEEE Transactions on Image Processing, vol. 17, no. 10, pp. 1940–1949, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Li, R. Huang, Z. Ding, J. C. Gatenby, D. N. Metaxas, and J. C. Gore, “A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI,” IEEE Transactions on Image Processing, vol. 20, no. 7, pp. 2007–2016, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Qian, J. Wang, S. Guo, and Q. Li, “An active contour model for medical image segmentation with application to brain CT image,” Medical Physics, vol. 40, no. 2, pp. 1911–1920, 2013. View at Google Scholar
  10. Y. Pan, K. Feng, D. Yang, Y. Feng, and Y. Wang, “A medical image segmentation based on global variational level set,” in Proceedings of the ICME International Conference on Complex Medical Engineering (CME '13), pp. 429–4432, 2013.
  11. Y. Yao and Y. Cheng, “High effective medical image segmentation with model adjustable method,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '13), pp. 1512–1515, 2013.
  12. G. W. Wei, “Generalized Perona-Malik equation for image restoration,” IEEE Signal Processing Letters, vol. 6, no. 7, pp. 165–167, 1999. View at Publisher · View at Google Scholar · View at Scopus
  13. G. W. Wei and Y. Q. Jia, “Synchronization-based image edge detection,” Europhysics Letters, vol. 59, no. 6, pp. 814–819, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Wang, G.-W. Wei, and S. Yang, “Partial differential equation transform-Variational formulation and Fourier analysis,” International Journal for Numerical Methods in Biomedical Engineering, vol. 27, no. 12, pp. 1996–2020, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. X. Bresson, S. Esedoglu, P. Vandergheynst, J.-P. Thiran, and S. Osher, “Fast global minimization of the active contour/snake model,” Journal of Mathematical Imaging and Vision, vol. 28, no. 2, pp. 151–167, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. L. A. Vese and T. F. Chan, “A multiphase level set framework for image segmentation using the Mumford and Shah model,” International Journal of Computer Vision, vol. 50, no. 3, pp. 271–293, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Simon, “Split bregman algorithm, douglas-rachford splitting and frame shrinkage,” in Scale Space and Variational Methods in Computer Vision, vol. 5567 of Lecture Notes in Computer Science, pp. 464–476, Springer, 2009. View at Google Scholar
  18. J. Yang, W. Yin, Y. Zhang, and Y. Wang, “A fast algorithm for edge-preserving variational multichannel image restoration,” SIAM Journal on Imaging Sciences, vol. 2, no. 2, pp. 569–592, 2009. View at Google Scholar
  19. D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. P. Meer and B. Georgescu, “Edge detection with embedded confidence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 12, pp. 1351–1365, 2001. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Christoudias, B. Georgescu, and P. Meer, “Synergism in low level vision,” in Proceedings of the 16th International Conference of Pattern Recognition, Quebec City, Canada, August 2001.