Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2007 (2007), Article ID 25182, 9 pages
http://dx.doi.org/10.1155/2007/25182
Research Article

Molecular Image Segmentation Based on Improved Fuzzy Clustering

Department of Electronic Engineering, Fudan University, Shanghai 200433, China

Received 18 January 2007; Revised 28 April 2007; Accepted 17 July 2007

Academic Editor: Jie Tian

Copyright © 2007 Jinhua Yu and Yuanyuan Wang. 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. A. Hengerer, A. Wunder, D. J. Wagenaar, A. H. Vija, M. Shah, and J. Grimm, “From genomics to clinical molecular imaging,” Proceedings of the IEEE, vol. 93, no. 4, pp. 819–828, 2005. View at Publisher · View at Google Scholar
  2. J C. Bezdek, “A convergence theorem for the fuzzy ISODATA clustering algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 2, no. 1, pp. 1–8, 1980. View at Google Scholar
  3. 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
  4. 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
  5. S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 34, no. 4, pp. 1907–1916, 2004. View at Publisher · View at Google Scholar
  6. Y. A. Tolias and S. M. Panas, “Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 28, no. 3, pp. 359–369, 1998. View at Publisher · View at Google Scholar
  7. A. W. C. Liew, H. Yan, and N.-F. Law, “Image segmentation based on adaptive cluster prototype estimation,” IEEE Transactions on Fuzzy Systems, vol. 13, no. 4, pp. 444–453, 2005. View at Publisher · View at Google Scholar
  8. J. Ling and A. C. Bovik, “Smoothing low-SNR molecular images via anisotropic median-diffusion,” IEEE Transactions on Medical Imaging, vol. 21, no. 4, pp. 377–384, 2002. View at Publisher · View at Google Scholar
  9. F. Russo, “A method for estimation and filtering of Gaussian noise in images,” IEEE Transactions on Instrumentation and Measurement, vol. 52, no. 4, pp. 1148–1154, 2003. View at Publisher · View at Google Scholar
  10. Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Transactions on Image Processing, vol. 11, no. 11, pp. 1260–1270, 2002. View at Publisher · View at Google Scholar
  11. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, 1990. View at Publisher · View at Google Scholar
  12. T. Randen and J. H. Husøy, “Filtering for texture classification: a comparative study,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291–310, 1999. View at Publisher · View at Google Scholar
  13. B. Schiele and J. L. Crowley, “Recognition without correspondence using multidimensional receptive field histograms,” International Journal of Computer Vision, vol. 36, no. 1, pp. 31–50, 2000. View at Publisher · View at Google Scholar
  14. C. Liu and H. Wechsler, “Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition,” IEEE Transactions on Image Processing, vol. 11, no. 4, pp. 467–476, 2002. View at Publisher · View at Google Scholar
  15. 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
  16. H. Li, J. Tian, F. Zhu et al., “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with the Monte Carlo method,” Academic Radiology, vol. 11, no. 9, pp. 1029–1038, 2004. View at Publisher · View at Google Scholar
  17. H. Li, J. Tian, J. Luo, G. Wang, and W. Cong, “Interactive graphic editing tools in bioluminescent imaging simulation,” in Photonic Therapeutics and Diagnostics, vol. 5686 of Proceedings of SPIE, pp. 407–414, San Jose, Calif, USA, January 2005. View at Publisher · View at Google Scholar
  18. Y. J. Lv, J. Tian, H. Li, W. Cong, and G. Wang, “Adaptive finite element methods for diffusive photon propagation in bioluminescent imaging,” in Proceedings of the 4th Annual Meeting of Molecular Imaging, p. 369, Cologne, Germany, September 2005.