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The Scientific World Journal
Volume 2014, Article ID 307416, 13 pages
http://dx.doi.org/10.1155/2014/307416
Research Article

Narrow Band Region-Based Active Contours Model for Noisy Color Image Segmentation

School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China

Received 23 April 2014; Revised 16 June 2014; Accepted 24 June 2014; Published 10 July 2014

Academic Editor: Cristiana Corsi

Copyright © 2014 Xiaomin Xie 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. Kurita, N. Otsu, and N. Abdelmalek, “Maximum likelihood thresholding based on population mixture models,” Pattern Recognition, vol. 25, no. 10, pp. 1231–1240, 1992. View at Publisher · View at Google Scholar · View at Scopus
  2. F. U. Siddiqui and N. A. M. Isa, “Enhanced moving K-means (EMKM) algorithm for image segmentation,” IEEE Transactions on Consumer Electronics, vol. 57, no. 2, pp. 833–841, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, vol. 22, no. 1, pp. 61–79, 1997. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization: a new variational formulation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 430–436, June 2005. View at Scopus
  5. C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Transactions on Image Processing, vol. 19, no. 12, pp. 3243–3254, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. 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 Zentralblatt MATH · View at Scopus
  7. T. F. Chan, B. Y. Sandberg, and L. A. Vese, “Active contours without edges for vector-valued images,” Journal of Visual Communication and Image Representation, vol. 11, no. 2, pp. 130–141, 2000. View at Publisher · View at Google Scholar · View at Scopus
  8. T. F. Chan and L. A. Vese, “An efficient variational multiphase motion for the Mumford-Shah segmentation model,” in Proceedings of the 34th Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 490–494, November 2000. View at Scopus
  9. 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
  10. S. Lankton and A. Tannenbaum, “Localizing region-based active contours,” IEEE Transactions on Image Processing, vol. 17, no. 11, pp. 2029–2039, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. C. Li, C. 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 MathSciNet · View at Scopus
  12. K. Zhang, H. Song, and L. Zhang, “Active contours driven by local image fitting energy,” Pattern Recognition, vol. 43, no. 4, pp. 1199–1206, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  13. X. Xie, C. Wang, A. Zhang, and X. Meng, “Active contours model exploiting hybrid image information: an improved formulation and level set method,” Journal of Computational Information Systems, vol. 9, no. 20, pp. 8371–8379, 2013. View at Google Scholar
  14. K. Wei, Z. L. Jing, Y. X. Li, and H. Y. Tuo, “Extended scheme of Chan-Vese models for colour image segmentation,” IET Image Processing, vol. 5, no. 7, pp. 583–597, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. L. Liu, L. Zeng, K. Shen, and X. Luan, “Exploiting local intensity information in Chan-Vese model for noisy image segmentation,” Signal Processing, vol. 93, no. 9, pp. 2709–2721, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. Q. Zheng and E. Q. Dong, “Narrow band active contour model for local segmentation of medical and texture images,” Acta Automatica Sinica, vol. 39, no. 1, pp. 21–30, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. Y. Zheng, G. Li, X. Sun, and X. Zhou, “Fast edge integration based active contours for color images,” Computers & Electrical Engineering, vol. 35, no. 1, pp. 141–149, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Yang and B. Wu, “A new and fast multiphase image segmentation model for color images,” Mathematical Problems in Engineering, vol. 2012, Article ID 494761, 20 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  19. J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Material Science, vol. 3 of Cambridge Monographs on Applied and Computational Mathematics, Cambridge University Press, Cambridge, UK, 2nd edition, 1999. View at MathSciNet
  20. M. Sussman, P. Smereka, and S. Osher, “A level set approach for computing solutions to incompressible two-phase flow,” Journal of Computational Physics, vol. 114, no. 1, pp. 146–159, 1994. View at Publisher · View at Google Scholar · View at Scopus
  21. U. Vovk, F. Pernus, and B. Likar, “A review of methods for correction of intensity inhomogeneity in MRI,” IEEE Transactions on Medical Imaging, vol. 26, no. 3, pp. 405–421, 2007. View at Google Scholar
  22. L. Wang, C. Li, Q. Sun, D. Xia, and C. Kao, “Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation,” Computerized Medical Imaging and Graphics, vol. 33, no. 7, pp. 520–531, 2009. View at Publisher · View at Google Scholar · View at Scopus