Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 747549, 13 pages
http://dx.doi.org/10.1155/2014/747549
Research Article

A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering

1School of Computer Science and Technology, Shandong University, Jinan 250101, China
2College of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan 250355, China

Received 22 November 2013; Accepted 9 January 2014; Published 9 March 2014

Academic Editor: Yuanjie Zheng

Copyright © 2014 Shiyong Ji 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. J. S. Duncan and N. Ayache, “Medical image analysis: progress over two decades and the challenges ahead,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 85–106, 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. 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), vol. 1, pp. 430–436, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Li, C. Xu, A. W. Anderson, and J. C. Gore, “MRI tissue classification and bias field estimation based on coherent local intensity clustering: a unified energy minimization framework,” in Information Processing in Medical Imaging: Proceedings of the 21st International Conference, IPMI 2009, Williamsburg, VA, USA, July 5–10, 2009, vol. 5636 of Lecture Notes in Computer Science, pp. 288–299, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar
  4. 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
  5. M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty, “A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193–199, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. L. Yi and G. Zhijun, “A review of segmentation method for MR image,” in Proceedings of the International Conference on Image Analysis and Signal Processing (IASP ’10), pp. 351–357, Zhejiang, China, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. L. Cheng, J. Yang, X. Fan, and Y. Zhu, “A generalized level set formulation of the mumford-shah functional for brain MR image segmentation,” in Information Processing in Medical Imaging, vol. 3565 of Lecture Notes in Computer Science, pp. 418–430, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  8. X. Fan, J. Yang, and L. Cheng, “A Novel segmentation method for MR brainimages based on fuzzy connectedness and FCM,” in Fuzzy Systems and Knowledge Discovery, vol. 3613 of Lecture Notes in Computer Science, pp. 505–513, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  9. X. Fan, J. Yang, Y. Zheng, L. Cheng, and Y. Zhu, “A novel unsupervised segmentation method for MR brain images based on fuzzy methods,” in Computer Vision for Biomedical Image Applications, vol. 3765 of Lecture Notes in Computer Science, pp. 160–169, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  10. C. Zhu and T. Jiang, “Multi-context fuzzy clustering for separation of brain tissues in magnetic resonance images,” NeuroImage, vol. 18, no. 3, pp. 685–696, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. D. L. Pham and J. L. Prince, “Adaptive fuzzy segmentation of magnetic resonance images,” IEEE Transactions on Medical Imaging, vol. 18, no. 9, pp. 737–752, 1999. View at Publisher · View at Google Scholar · View at Scopus
  12. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY, USA, 1981.
  13. L. Amini, H. Soltanian-Zadeh, C. Lucas, and M. Gity, “Automatic segmentation of thalamus from brain MRI integrating fuzzy clustering and dynamic contours,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 5, pp. 800–811, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Shen, W. Sandham, M. Granat, and A. Sterr, “MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization,” IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 3, pp. 459–467, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. S. P. Awate, H. Zhang, and J. C. Gee, “A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction,” IEEE Transactions on Medical Imaging, vol. 26, no. 11, pp. 1525–1536, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Halt, C. C. le Rest, A. Turzo, C. Roux, and D. Visvikis, “A fuzzy locally adaptive bayesian segmentation approach for volume determination in PET,” IEEE Transactions on Medical Imaging, vol. 28, no. 6, pp. 881–893, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. X. F. Zhang, C. M. Zhang, W. J. Tang, and Z. W. Wei, “Medical image segmentation using improved FCM,” Science China Information Sciences, vol. 55, no. 5, pp. 1052–1061, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC superpixels compared to state-of-the-art superpixel methods,” IEEE Transactions on Patten Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274–2281, 2012. View at Publisher · View at Google Scholar
  19. W. Wu, A. Y. C. Chen, L. Zhao, and J. J. Corso, “Brain tumor detection and segmentation in a CRF, (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features,” International Journal of Computer Assisted Radiology and Surgery, 2013. View at Publisher · View at Google Scholar
  20. M. Li, H. He, J. Yi, B. Lv, and M. Zhao, “Segmentation and tracking of coronary artery using graph-cut in CT angiographic,” in Proceedings of the 2nd International Conference on Biomedical Engineering and Informatics (BMEI ’09), pp. 1–4, Tianjin, China, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Zhang and Q. Ji, “Image segmentation with a unified graphical model,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 8, pp. 1406–1425, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. L. Gan, Y. Wu, M. Liu, P. Zhang, H. Ji, and F. Wang, “Triplet markov fields with edge location for fast unsupervised multi-class segmentation of synthetic aperture radar images,” IET Image Processing, vol. 6, no. 7, pp. 831–838, 2012. View at Publisher · View at Google Scholar
  23. X. Zhou, X. Li, T.-J. Chin, and D. Suter, “Superpixel-driven level set tracking,” in Proceedings of the 19th IEEE International Conference on Image Processing (ICIP ’12), pp. 409–412, Orlando, Fla, USA, October 2012. View at Publisher · View at Google Scholar
  24. X. Gong and J. Liu, “Rock detection via superpixel graph cuts,” in Proceedings of the 19th IEEE International Conference on Image Processing (ICIP ’12), pp. 2149–2152, Orlando, Fla, USA, October 2012. View at Publisher · View at Google Scholar
  25. H. Jiang, “Solution to scale invariant global figure ground separation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’12), pp. 678–685, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. Z. Li, X.-M. Wu, and S.-F. Chang, “Segmentation using superpixels: a bipartite graph partitioning approach,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’12), pp. 789–796, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar
  27. A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, “Turbopixels: fast superpixels using geometric flows,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2290–2297, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583–598, 1991. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, 2000. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Vedaldi and S. Soatto, “Quick shift and kernel methods for mode seeking,” in Computer Vision—ECCV: Proceedings of the 10th European Conference on Computer Vision, Marseille, France, October 12–18, 2008, Part IV, vol. 5305 of Lecture Notes in Computer Science, pp. 705–718, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  31. http://www.bic.mni.mcgill.ca/brainweb/.