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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 132953, 13 pages
http://dx.doi.org/10.1155/2013/132953
Research Article

Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation

Universidad de Guanajuato, División de Ingenierías, Campus Irapuato-Salamanca, Carretera Salamanca, Valle de Santiago km 3.5+1.8, Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico

Received 21 December 2012; Revised 8 April 2013; Accepted 9 April 2013

Academic Editor: Peng Feng

Copyright © 2013 I. Cruz-Aceves 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. H. Castillejos, V. Ponomaryov, L. N. de Rivera, and V. Golikov, “Wavelet transform fuzzy algorithms for dermoscopic image segmentation,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 578721, 11 pages, 2012. View at Publisher · View at Google Scholar
  2. P. Davuluri, J. Wu, Y. Tang et al., “Hemorrhage detection and segmentation in traumatic pelvic injuries,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 898430, 12 pages, 2012. View at Publisher · View at Google Scholar
  3. A. Nyma, M. Kang, Y. Kwon, C. Kim, and J. Kim, “A hybrid technique for medical image segmentation,” Journal of Biomedicine and Biotechnology, vol. 2012, Article ID 830252, 7 pages, 2012. View at Publisher · View at Google Scholar
  4. W. Hsu, “Improved watershed transform for tumor segmentation: application to mammogram image compresion,” Expert Systems With Applications, vol. 39, no. 4, pp. 3950–3955, 2012. View at Publisher · View at Google Scholar
  5. E. Cuevas, V. Osuna-Enciso, D. Zaldivar, M. Perez-Cisneros, and H. Sossa, “Multithreshold segmentation based on artificial immune systems,” Mathematical Problems in Engineering, vol. 2012, Article ID 874761, 20 pages, 2012. View at Publisher · View at Google Scholar
  6. Y. Boykov and M. Jolly, “Interactive organ segmentation using graph cuts,” in Proceedings of the Medical Image Computing and Computer-Assisted Intervention, pp. 276–286, 2000.
  7. F. R. Schmidt, E. Töppe, and D. Cremers, “Efficient planar graph cuts with applications in computer vision,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops '09), pp. 351–356, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. D. R. Chen, R. F. Chang, W. J. Wu, W. K. Moon, and W. L. Wu, “3-D breast ultrasound segmentation using active contour model,” Ultrasound in Medicine and Biology, vol. 29, no. 7, pp. 1017–1026, 2003. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Jumaat, W. Rahman, A. Ibrahim, and R. Mahmud, “Segmentation of masses from breast ultrasound images using parametric active contour algorithm,” Procedia Social and Behavioral Sciences, vol. 8, pp. 640–647, 2010. View at Publisher · View at Google Scholar
  10. M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” International Journal of Computer Vision, vol. 1, no. 4, pp. 321–331, 1988. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Pang, L. Li, W. Hu, Y. Peng, L. Liu, and Y. Shao, “Computerized segmentation and characterization of breast lesions in dynamic contrast-enhanced mr images using fuzzy c-means clustering and snake algorithm,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 634907, 10 pages, 2012. View at Publisher · View at Google Scholar
  12. L. Gao, X. Liu, and W. Chen, “Phase- and gvf-based level set segmentation of ultrasonic breast tumors,” Journal of Applied Mathematics, vol. 2012, Article ID 810805, 22 pages, 2012. View at Publisher · View at Google Scholar
  13. X. Liu, M. Haider, and I. Yetik, “Unsupervised 3d prostate segmentation based on diffusion-weighted imaging mri using active contour models with a shape prior,” Journal of Electrical and Computer Engineering, vol. 2011, Article ID 410912, 11 pages, 2011. View at Publisher · View at Google Scholar
  14. X. Zhu, P. Zhang, J. Shao, Y. Cheng, Y. Zhang, and J. Bai, “A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation,” Ultrasonics, vol. 51, no. 2, pp. 181–189, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. I. Dagher and K. E. Tom, “Waterballoons: a hybrid watershed balloon snake segmentation,” Image and Vision Computing, vol. 26, no. 7, pp. 905–912, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Wang, L. He, A. Mishra, and C. Li, “Active contours driven by local gaussian distribution fitting energy,” Signal Processing, vol. 89, no. 12, pp. 2435–2447, 2009. View at Publisher · View at Google Scholar
  17. B. Liu, H. Cheng, J. Huang, J. Tian, X. Tang, and J. Liu, “Probability density difference-based active contour for ultrasound image segmentation,” Pattern Recognition, vol. 43, no. 6, pp. 2028–2042, 2010. View at Publisher · View at Google Scholar
  18. X. Chen, J. Udupa, U. Bagci, Y. Zhuge, and J. Yao, “Medical image segmentation by combining graph cuts and oriented active appearance models,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 2035–2046, 2012. View at Publisher · View at Google Scholar
  19. M. Talebi, A. Ayatollahi, and A. Kermani, “Medical ultrasound image segmentation using genetic active contour,” Journal of Biomedical Science and Engineering, vol. 4, pp. 105–109, 2011. View at Publisher · View at Google Scholar
  20. J. Novo, J. Santos, and M. Penedo, “Topological active models optimization with differential evolution,” Expert Systems With Applications, vol. 39, no. 15, pp. 12165–12176, 2012. View at Publisher · View at Google Scholar
  21. C. C. Tseng, J. G. Hsieh, and J. H. Jeng, “Active contour model via multi-population particle swarm optimization,” Expert Systems with Applications, vol. 36, no. 3, pp. 5348–5352, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. E. Shahamatnia and M. Ebadzadeh, “Application of particle swarm optimization and snake model hybrid on medical imaging,” in Proceedings of the 3rd International Workshop on Computational Intelligence in Medical Imaging (CIMI '11), pp. 1–8, IEEE service center, 2011.
  23. R. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43, October 1995. View at Scopus
  24. Y. Shi and R. Eberhart, “Modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '98), pp. 69–73, May 1998. View at Scopus
  25. W. He, Y. Cheng, L. Xia, and F. Liu, “A new particle swarm optimization-based method for phase unwrapping of mri data,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 475745, 9 pages, 2012. View at Publisher · View at Google Scholar
  26. M. J. Abdi, S. Hosseini, and M. Rezghi, “A novel weighted support vector machine based on particle swarm optimization for gene selection and tumor classification,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 320698, 7 pages, 2012. View at Publisher · View at Google Scholar