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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 395071, 15 pages
http://dx.doi.org/10.1155/2013/395071
Research Article

White Blood Cell Segmentation by Circle Detection Using Electromagnetism-Like Optimization

Erik Cuevas,1 Diego Oliva,2 Margarita Díaz,1 Daniel Zaldivar,1 Marco Pérez-Cisneros,1 and Gonzalo Pajares2

1Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenida Revolución 1500, 44430 Guadalajara, JAL, Mexico
2Departamento de Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, Avenida Complutense S/N, 28040 Madrid, Spain

Received 21 October 2012; Revised 21 December 2012; Accepted 29 December 2012

Academic Editor: Yoram Louzoun

Copyright © 2013 Erik Cuevas 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. Liu and K. C. Tsui, “Toward nature-inspired computing,” Communications of the ACM, vol. 49, no. 10, pp. 59–64, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Wang and R. Chu, “A novel white blood cell detection method based on boundary support vectors,” in Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC '09), pp. 2595–2598, San Antonio, Tex, USA, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Wu, P. Zeng, Y. Zhou, and C. Olivier, “A novel color image segmentation method and its application to white blood cell image analysis,” in Proceedings of the 8th International Conference on Signal Processing (ICSP '06), November 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Wang, F. L. Korris, and D. Fu, “Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection,” Neurocomputing, vol. 70, no. 7–9, pp. 1348–1359, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. G. Karkavitsas and M. Rangoussi, “Object localization in medical images using genetic algorithms,” World Academy of Science, Engineering and Technology, vol. 2, pp. 499–502, 2007.
  6. H. Muammar and M. Nixon, “Approaches to extending the Hough transform,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP '89), vol. 3, pp. 1556–1559, May 1989. View at Scopus
  7. T. Atherton and D. Kerbyson, “Using phase to represent radius in the coherent circle Hough transform,” in Proceedings of IEE Colloquium on the Hough Transform, pp. 1–4, May 1993.
  8. M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Shaked, O. Yaron, and N. Kiryati, “Deriving stopping rules for the probabilistic Hough transform by sequential analysis,” Computer Vision and Image Understanding, vol. 63, no. 3, pp. 512–526, 1996. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Xu, E. Oja, and P. Kultanen, “A new curve detection method: randomized Hough transform (RHT),” Pattern Recognition Letters, vol. 11, no. 5, pp. 331–338, 1990. View at Scopus
  11. J. H. Han, L. T. Koczy, and T. Poston, “Fuzzy Hough transform,” in Proceedings of the 2nd IEEE International Conference on Fuzzy Systems, vol. 2, pp. 803–808, April 1993. View at Scopus
  12. J. M. Becker, S. Grousson, and D. Coltuc, “From Hough transform to integral geometry,” in Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS '02), vol. 3, pp. 1444–1446, June 2002. View at Scopus
  13. V. Ayala-Ramirez, C. H. Garcia-Capulin, A. Perez-Garcia, and R. E. Sanchez-Yanez, “Circle detection on images using genetic algorithms,” Pattern Recognition Letters, vol. 27, no. 6, pp. 652–657, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. E. Cuevas, N. Ortega-Sánchez, D. Zaldivar, and M. Pérez-Cisneros, “Circle detection by harmony search optimization,” Journal of Intelligent & Robotic Systems, vol. 66, no. 3, pp. 359–376, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Cuevas, D. Zaldivar, M. Pérez-Cisneros, and M. Ramírez-Ortegón, “Circle detection using discrete differential evolution optimization,” Pattern Analysis & Applications, vol. 14, no. 1, pp. 93–107, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. E. Cuevas, D. Oliva, D. Zaldivar, M. Pérez-Cisneros, and H. Sossa, “Circle detection using electro-magnetism optimization,” Information Sciences, vol. 182, no. 1, pp. 40–55, 2012.
  17. G. Karkavitsas and M. Rangoussi, “Object localization in medical images using genetic algorithms,” International Journal of Information and Communication Engineering, vol. 1, no. 4, pp. 204–207, 2005.
  18. S. Birbil and S. C. Fang, “An electromagnetism-like mechanism for global optimization,” Journal of Global Optimization, vol. 25, no. 3, pp. 263–282, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. A. M. A. C. Rocha and E. M. G. P. Fernandes, “Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems,” International Journal of Computer Mathematics, vol. 86, no. 10-11, pp. 1932–1946, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Birbil, S. C. Fang, and R. L. Sheu, “On the convergence of a population-based global optimization algorithm,” Journal of Global Optimization, vol. 30, no. 2-3, pp. 301–318, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. E. W. Cowan, Basic Electromagnetism, Academic Press, New York, NY, USA, 1968.
  22. J. Bresenham, “Linear algorithm for incremental digital display of circular arcs,” Communications of the ACM, vol. 20, no. 2, pp. 100–106, 1977. View at Publisher · View at Google Scholar · View at Scopus
  23. G. Boccignone, M. Ferraro, and P. Napoletano, “Diffused expectation maximisation for image segmentation,” Electronics Letters, vol. 40, no. 18, pp. 1107–1108, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. G. Boccignone, P. Napoletano, V. Caggiano, and M. Ferraro, “A multiresolution diffused expectation-maximization algorithm for medical image segmentation,” Computers in Biology and Medicine, vol. 37, no. 1, pp. 83–96, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison Wesley, Reading, Mass, USA, 1992.
  26. G. Landi and E. L. Piccolomini, “An efficient method for nonnegatively constrained Total Variation-based denoising of medical images corrupted by Poisson noise,” Computerized Medical Imaging and Graphics, vol. 36, no. 1, pp. 38–46, 2012. View at Publisher · View at Google Scholar