Table of Contents
Journal of Computational Medicine
Volume 2013, Article ID 972970, 7 pages
http://dx.doi.org/10.1155/2013/972970
Research Article

Automatic Segmentation of Medical Images Using Fuzzy c-Means and the Genetic Algorithm

Medical Intelligence Lab, Department of Computer Engineering, Bu Ali Sina University, Hamedan, Iran

Received 14 December 2012; Revised 17 January 2013; Accepted 17 January 2013

Academic Editor: Hiroshi Watabe

Copyright © 2013 Omid Jamshidi and Abdol Hamid Pilevar. 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. D. N. Chun and H. S. Yang, “Robust image segmentation using genetic algorithm with a fuzzy measure,” Pattern Recognition, vol. 29, no. 7, pp. 1195–1211, 1996. View at Publisher · View at Google Scholar · View at Scopus
  2. R. A. Heckemann, J. V. Hajnal, P. Aljabar, D. Rueckert, and A. Hammers, “Automatic anatomical brain MRI segmentation combining label propagation and decision fusion,” NeuroImage, vol. 33, no. 1, pp. 115–126, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. M. Wang, Y. C. Soh, Q. Song, and K. Sim, “Adaptive spatial information-theoretic clustering for image segmentation,” Pattern Recognition, vol. 42, no. 9, pp. 2029–2044, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. S. R. Kannan, S. Ramathilagam, R. Devi, and E. Hines, “Strong fuzzy c-means in medical image data analysis,” Journal of Systems and Software, vol. 85, no. 11, pp. 2425–2438, 2012. View at Publisher · View at Google Scholar
  5. S. R. Kannan, S. Ramathilagam, R. Devi, and A. Sathya, “Robust kernel FCM in segmentation of breast medical images,” Expert Systems with Applications, vol. 38, no. 4, pp. 4382–4389, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. S. R. Kannan, A. Sathya, S. Ramathilagam, and R. Devi, “Novel segmentation algorithm in segmenting medical images,” Journal of Systems and Software, vol. 83, no. 12, pp. 2487–2495, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Caldairou, N. Passat, P. A. Habas, C. Studholme, and F. Rousseau, “A non-local fuzzy segmentation method: application to brain MRI,” Pattern Recognition, vol. 44, no. 9, pp. 1916–1927, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. He, M. Y. Hussaini, J. Ma, B. Shafei, and G. Steidl, “A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data,” Pattern Recognition, vol. 45, no. 9, pp. 3463–3471, 2012. View at Publisher · View at Google Scholar
  9. Z.-X. Ji, Q.-S. Sun, and D. S. Xia, “A framework with modified fast FCM for brain MR images segmentation,” Pattern Recognition, vol. 44, no. 5, pp. 999–1013, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. Z.-X. Ji, Q.-S. Sun, and D. S. Xia, “A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image,” Computerized Medical Imaging and Graphics, vol. 35, no. 5, pp. 383–397, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. Ji, Q. Sun, Y. Xia, Q. Chen, D. Xia, and D. Feng, “Generalized rough fuzzy c-means algorithm for brain MR image segmentation,” Computer Methods and Programs in Biomedicine, vol. 108, no. 2, pp. 644–655, 2011. View at Publisher · View at Google Scholar
  12. J. Wang, J. Kong, Y. Lu, M. Qi, and B. Zhang, “A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints,” Computerized Medical Imaging and Graphics, vol. 32, no. 8, pp. 685–698, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. K. Sikka, N. Sinha, P. K. Singh, and A. K. Mishra, “A fully automated algorithm under modified FCM framework for improved brain MR image segmentation,” Magnetic Resonance Imaging, vol. 27, no. 7, pp. 994–1004, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. C.-C. Lai and C.-Y. Chang, “A hierarchical evolutionary algorithm for automatic medical image segmentation,” Expert Systems with Applications, vol. 36, no. 1, pp. 248–259, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Y. Yeh and J. C. Fu, “A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI,” Expert Systems with Applications, vol. 34, no. 2, pp. 1285–1295, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. J. C. Fu, C. C. Chen, J. W. Chai, S. T. C. Wong, and I. C. Li, “Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging,” Computerized Medical Imaging and Graphics, vol. 34, no. 4, pp. 308–320, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. H.-C. Chen and W.-J. Wang, “Efficient impulse noise reduction via local directional gradients and fuzzy logic,” Fuzzy Sets and Systems, vol. 160, no. 13, pp. 1841–1857, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. W.-B. Tao, J.-W. Tian, and J. Liu, “Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm,” Pattern Recognition Letters, vol. 24, no. 16, pp. 3069–3078, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. A. R. Van Erkel and P. M. T. Pattynama, “Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology,” European Journal of Radiology, vol. 27, no. 2, pp. 88–94, 1998. View at Publisher · View at Google Scholar · View at Scopus
  20. J. K. Udupa, V. R. LeBlanc, Y. Zhuge et al., “A framework for evaluating image segmentation algorithms,” Computerized Medical Imaging and Graphics, vol. 30, no. 2, pp. 75–87, 2006. View at Publisher · View at Google Scholar · View at Scopus