Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 419018, 9 pages
http://dx.doi.org/10.1155/2013/419018
Research Article

Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

1División de Ingenierías Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago Km 3.5+1.8 Km Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico
2Centro de Investigación en Matemáticas (CIMAT), A.C. Jalisco S/N, Col. Valenciana, 36000 Guanajuato, GTO, Mexico

Received 19 July 2013; Accepted 23 October 2013

Academic Editor: Marco Perez-Cisneros

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. 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.
  2. 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 '09), pp. 351–356, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. W.-Y. Hsu, “Improved watershed transform for tumor segmentation: application to mammogram image compression,” Expert Systems with Applications, vol. 39, no. 4, pp. 3950–3955, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. 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
  5. M. Guijarro, G. Pajares, I. Riomoros, P. J. Herrera, X. P. Burgos-Artizzu, and A. Ribeiro, “Automatic segmentation of relevant textures in agricultural images,” Computers and Electronics in Agriculture, vol. 75, no. 1, pp. 75–83, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. E. Cuevas, D. Zaldivar, and M. Pérez-Cisneros, “A novel multi-threshold segmentation approach based on differential evolution optimization,” Expert Systems with Applications, vol. 37, no. 7, pp. 5265–5271, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. 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
  8. S. Ahmadi, M. J. V. Zoej, H. Ebadi, H. A. Moghaddam, and A. Mohammadzadeh, “Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours,” International Journal of Applied Earth Observation and Geoinformation, vol. 12, no. 3, pp. 150–157, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. B. Wu and Y. Yang, “Local- and global-statistics-based active contour model for image segmentation,” Mathematical Problems in Engineering, vol. 2012, Article ID 791958, 16 pages, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  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. T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models-their training and application,” Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38–59, 1995. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Hwang, J. Kim, Y. Han, and H. Park, “An automatic cerebellum extraction method in T1-weighted brain MR images using an active contour model with a shape prior,” Magnetic Resonance Imaging, vol. 29, no. 7, pp. 1014–1022, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. 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
  14. 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
  15. I. Cruz-Aceves, J. Avina-Cervantes, J. Lopez-Hernandez et al., “Multiple active contours guided by differential evolution for medical image segmentation,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 190304, 14 pages, 2013. View at Publisher · View at Google Scholar
  16. 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
  17. M. Hauschild and M. Pelikan, “An introduction and survey of estimation of distribution algorithms,” Swarm and Evolutionary Computation, vol. 1, no. 3, pp. 111–128, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Santana, P. Larrañaga, and J. A. Lozano, “Side chain placement using estimation of distribution algorithms,” Artificial Intelligence in Medicine, vol. 39, no. 1, pp. 49–63, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. W. Yan and L. Xiaoxiong, “An improved univariate marginal distribution algorithm for dynamic optimization problem,” AASRI Procedia, vol. 1, pp. 166–170, 2012. View at Publisher · View at Google Scholar
  20. A. Petrovski, S. Shakya, and J. McCall, “Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms,” in Proceedings of the 8th Annual Genetic and Evolutionary Computation Conference (GECCO '06), pp. 413–418, July 2006. View at Scopus
  21. R. Shah and P. Reed, “Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems,” European Journal of Operational Research, vol. 211, no. 3, pp. 466–479, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  22. A. Tsai, A. Yezzi Jr., W. Wells et al., “A shape-based approach to the segmentation of medical imagery using level sets,” IEEE Transactions on Medical Imaging, vol. 22, no. 2, pp. 137–154, 2003. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Mühlenbein and G. Paaß, “From recombination of genes to the estimation of distributions i. binary parameters,” in Parallel Problem Solving From Nature, pp. 178–187, Springer, 1996. View at Google Scholar
  24. P. Larrañaga and J. Lozano, Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, Kluwer Academic, Boston, Mass, USA, 2002.
  25. M. Pelikan, D. E. Goldberg, and F. G. Lobo, “A survey of optimization by building and using probabilistic models,” Computational Optimization and Applications, vol. 21, no. 1, pp. 5–20, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  26. H. Mühlenbein, “The equation for response to selection and its use for prediction,” Evolutionary Computation, vol. 5, no. 3, pp. 303–346, 1997. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Bashir, M. Naeem, and S. I. Shah, “A comparative study of heuristic algorithms: GA and UMDA in spatially multiplexed communication systems,” Engineering Applications of Artificial Intelligence, vol. 23, no. 1, pp. 95–101, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. L.-V. Lozada-Chang and R. Santana, “Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints,” Information Sciences, vol. 181, no. 11, pp. 2340–2355, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  29. L. D. Cohen and I. Cohen, “Finite-element methods for active contour models and balloons for 2-D and 3-D images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1131–1147, 1993. View at Publisher · View at Google Scholar · View at Scopus