Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 9514707, 12 pages
http://dx.doi.org/10.1155/2016/9514707
Research Article

Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method

Yan Li,1,2 Rui Zhu,1 Lei Mi,1 Yihui Cao,1,2 and Di Yao3

1State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi’an 710119, China
2University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China
3Shenzhen Vivolight Medical Device and Technology Co., Ltd., Shenzhen 518000, China

Received 30 December 2015; Revised 7 April 2016; Accepted 21 April 2016

Academic Editor: Jayaram K. Udupa

Copyright © 2016 Yan Li 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. V. Piuri and F. Scotti, “Morphological classification of blood leucocytes by microscope images,” in Proceedings of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA '04), pp. 103–108, Boston, Mass, USA, July 2004. View at Scopus
  2. F. Scotti, “Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images,” in Proceedings of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA '05), pp. 96–101, Messian, Italy, July 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Fatichah, M. L. Tangel, M. R. Widyanto, F. Dong, and K. Hirota, “Interest-based ordering for fuzzy morphology on white blood cell image segmentation,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 16, no. 1, pp. 76–86, 2012. View at Google Scholar · View at Scopus
  4. I. Cseke, “A fast segmentation scheme for white blood cell images,” in Proceedings of the 11th IAPR International Conference on Pattern Recognition C: Image, Speech and Signal Analysis, vol. 3, pp. 530–533, IEEE, 1992.
  5. N. Otsu, “A threshold selection method from gray-level histograms,” Automatica, vol. 11, no. 285–296, pp. 23–27, 1975. View at Google Scholar
  6. J. H. Wu, P. 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), vol. 2, IEEE, Beijing, China, November 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Duan and L. Yu, “A WBC segmentation methord based on HSI color space,” in Proceedings of the 4th IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT '11), pp. 629–632, Shenzhen, China, 2011.
  8. K. Jiang, Q.-M. Liao, and Y. Xiong, “A novel white blood cell segmentation scheme based on feature space clustering,” Soft Computing, vol. 10, no. 1, pp. 12–19, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. L. B. Dorini, R. Minetto, and N. J. Leite, “Semiautomatic white blood cell segmentation based on multiscale analysis,” IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 1, pp. 250–256, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Arslan, E. Ozyurek, and C. Gunduz-Demir, “A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images,” Cytometry A, vol. 85, no. 6, pp. 480–490, 2014. View at Google Scholar
  11. L. B. Dorini, R. Minetto, and N. J. Leite, “White blood cell segmentation using morphological operators and scale-space analysis,” in Proceedings of the 20th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI '07), pp. 294–301, Minas Gerais, Brazil, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Saraswat and K. V. Arya, “Automated microscopic image analysis for leukocytes identification: a survey,” Micron, vol. 65, pp. 20–33, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. N. N. Guo, L. B. Zeng, and Q. S. Wu, “A method based on multispectral imaging technique for white blood cell segmentation,” Computers in Biology and Medicine, vol. 37, no. 1, pp. 70–76, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Zhang, X. Xiao, X. Li et al., “White blood cell segmentation by color-space-based k-means clustering,” Sensors, vol. 14, no. 9, pp. 16128–16147, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Mohapatra and D. Patra, “Automated leukemia detection using hausdorff dimension in blood microscopic images,” in Proceedings of the International Conference on IEEE Robotics and Communication Technologies (INTERACT '10), pp. 64–68, Chennai, India, December 2010. View at Publisher · View at Google Scholar
  16. N. M. Salem, “Segmentation of white blood cells from microscopic images using K-means clustering,” in Proceedings of the 31st National Radio Science Conference (NRSC '14), pp. 371–376, Cairo, Egypt, April 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. P. K. Mondal, U. K. Prodhan, M. S. Al Mamun et al., “Segmentation of white blood cells using fuzzy C means segmentation algorithm,” IOSR Jornal of Computer Engineering, vol. 1, no. 16, pp. 1–5, 2014. View at Google Scholar
  18. B. C. Ko, J.-W. Gim, and J.-Y. Nam, “Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake,” Micron, vol. 42, no. 7, pp. 695–705, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Hamghalam, M. Motameni, and A. E. Kelishomi, “Leukocyte segmentation in giemsa-stained image of peripheral blood smears based on active contour,” in Proceedings of the International Conference on Signal Processing Systems (ICSPS '09), pp. 103–106, IEEE, Singapore, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Di Rubeto, A. Dempster, S. Khan, and B. Jarra, “Segmentation of blood images using morphological operators,” in Proceedings of the 15th International Conference on Pattern Recognition, vol. 3, pp. 397–400, IEEE, Barcelona, Spain, September 2000.
  21. F. Scotti, “Robust segmentation and measurements techniques of white cells in blood microscope images,” in Proceedings of the IEEE Instrumentation and Measurement Technology Conference (IMTC '06), pp. 43–48, IEEE, Sorrento, Italy, 2006.
  22. K. A. Eldahshan, M. I. Youssef, E. H. Masameer, and M. A. Mustafa, “Segmentation framework on digital microscope images for acute lymphoblastic leukemia diagnosis based on HSV Color Space,” International Journal of Computer Applications, vol. 90, no. 7, pp. 48–51, 2014. View at Publisher · View at Google Scholar
  23. K. A. ElDahshan, M. I. Youssef, E. H. Masameer, and M. A. Hassan, “Comparison of segmentation framework on digital microscope images for acute lymphoblastic leukemia diagnosis using RGB and HSV color spaces,” Journal of Biomedical Engineering and Medical Imaging, vol. 2, no. 2, pp. 26–34, 2015. View at Publisher · View at Google Scholar
  24. V. Singhal and P. Singh, “Correlation based feature selection for diagnosis of acute lymphoblastic leukemia,” in Proceedings of the 3rd ACM International Symposium on Women in Computing and Informatics (WCI '15), pp. 5–9, Kochi, India, August 2015. View at Publisher · View at Google Scholar
  25. S. I. Sahidan, M. Y. Mashor, A. S. W. Wahab et al., “Local and global contrast stretching for color contrast enhancement on ziehl-neelsen tissue section slide images,” in 4th Kuala Lumpur International Conference on Biomedical Engineering 2008: BIOMED 2008 25–28 June 2008 Kuala Lumpur, Malaysia, vol. 21 of IFMBE Proceedings, pp. 583–586, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  26. R. C. Gonzalez and R. E. Woods, “Color image processing,” in Digital Image Processing, pp. 416–478, Prentice Hall, London, UK, 2002. View at Google Scholar
  27. R. D. Labati, V. Piuri, and F. Scotti, “All-IDB: the acute lymphoblastic leukemia image database for image processing,” in Proceedings of the 18th IEEE International Conference on Image Processing (ICIP '11), pp. 2045–2048, IEEE, Brussels, Belgium, September 2011. View at Publisher · View at Google Scholar · View at Scopus