Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 5689346, 12 pages
http://dx.doi.org/10.1155/2016/5689346
Research Article

A Novel Method for the Separation of Overlapping Pollen Species for Automated Detection and Classification

1Departamento de Posgrado, Instituto Tecnológico Superior de Lerdo, Tecnológico 1555, Placido Domingo, 35150 Lerdo, DG, Mexico
2Departamento de Posgrado, Instituto Tecnológico de la Laguna, Boulevard Revolución, Centro, 27000 Torreón, CO, Mexico

Received 9 December 2015; Accepted 15 February 2016

Academic Editor: Kazuhisa Nishizawa

Copyright © 2016 Santiago Tello-Mijares and Francisco Flores. 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. V. A. Martínez Ordaz, C. Rincón Castañeda, C. López Campos, V. M. Velasco Rodríguez, J. G. Lazo Saens, and P. Cano Ríos, “Asthmatic exacerbations and environmental pollen concentration in La Comarca Lagunera (Mexico),” Revista Alergia México, vol. 45, no. 4, pp. 106–111, 1998. View at Google Scholar
  2. C. L. Campos, C. B. R. Castañeda, V. B. Aburto et al., “Función respiratoria en niños asmáticos alérgicos y su relación con la concentración ambiental de polen,” Revista Alergia México, vol. 50, no. 4, pp. 129–146, 2003. View at Google Scholar
  3. M. Rodriguez-Damian, E. Cernadas, A. Formella, M. Fernandez-Delgado, and P. De Sa-Otero, “Automatic detection and classification of grains of pollen based on shape and texture,” IEEE Transactions on Systems, Man and Cybernetics C: Applications and Reviews, vol. 36, no. 4, pp. 531–542, 2006. View at Publisher · View at Google Scholar
  4. M. Ranzato, P. E. Taylor, J. M. House, R. C. Flagan, Y. LeCun, and P. Perona, “Automatic recognition of biological particles in microscopic images,” Pattern Recognition Letters, vol. 28, no. 1, pp. 31–39, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Mitsumoto, K. Yabusaki, and H. Aoyagi, “Classification of pollen species using autofluorescence image analysis,” Journal of Bioscience and Bioengineering, vol. 107, no. 1, pp. 90–94, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Kaya, S. M. Pinar, M. E. Erez, and M. Fidan, “An expert classification system of pollen of Onopordum using a rough set approach,” Review of Palaeobotany and Palynology, vol. 189, pp. 50–56, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Dell'Anna, P. Lazzeri, M. Frisanco et al., “Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning,” Analytical and Bioanalytical Chemistry, vol. 394, no. 5, pp. 1443–1452, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. R. P. Wodehouse, “Pollen grains. Their structure, identification and significance in science and medicine,” The Journal of Nervous and Mental Disease, vol. 86, no. 1, p. 104, 1937. View at Google Scholar
  9. V. E. Swanly, L. Selvam, P. M. Kumar, J. A. Renjith, M. Arunachalam, and K. L. Shunmuganathan, “Smart spotting of pulmonary TB cavities using CT images,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 864854, 12 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Fukunaga and L. D. Hostetler, “The estimation of the gradient of a density function, with application in pattern recognition,” IEEE Transactions on Information Theory, vol. 21, no. 1, pp. 32–40, 1975. View at Google Scholar · View at Scopus
  11. D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Otsu, “A threshold selection method from gray-level histograms,” Automatica, vol. 11, no. 285–296, pp. 23–27, 1975. View at Google Scholar
  13. 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
  14. C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 359–369, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  15. R. M. Haralick, “Statistical and structural approaches to texture,” Proceedings of the IEEE, vol. 67, no. 5, pp. 786–804, 1979. View at Publisher · View at Google Scholar · View at Scopus
  16. E. J. Mariarputham and A. Stephen, “Nominated texture based cervical cancer classification,” Computational and Mathematical Methods in Medicine, vol. 2015, Article ID 586928, 10 pages, 2015. View at Publisher · View at Google Scholar
  17. H. Liu, Y. Shao, D. Guo, Y. Zheng, Z. Zhao, and T. Qiu, “Cirrhosis classification based on texture classification of random features,” Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 536308, 8 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Holmes, A. Donkin, and I. H. Witten, “Weka: a machine learning workbench,” in Proceedings of the 2nd Australian and New Zealand Conference on Intelligent Information Systems, pp. 357–361, Brisbane, Australia, December 1994. View at Publisher · View at Google Scholar
  19. S. R. Garner, S. J. Cunningham, G. Holmes, C. G. Nevill-Manning, and I. H. Witten, “Applying a machine learning workbench: experience with agricultural databases,” in Proceedings of the Machine Learning in Practice Workshop, Machine Learning Conference, Tahoe City, Calif, USA, 1995.
  20. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  21. D. M. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011. View at Google Scholar