Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 501206, 13 pages
http://dx.doi.org/10.1155/2014/501206
Research Article

An Automatic Indirect Immunofluorescence Cell Segmentation System

1Department of Management Information Systems, National Chung Hsing University, Taichung 402, Taiwan
2Department of Computer Science and Engineering, National Chung Hsing University, Taichung 402, Taiwan
3Department of Science and Biotechnology, China Medical University, Taichung 402, Taiwan
4Department of Computer Science, University of Munster, 48149 Münster, Germany

Received 26 February 2014; Accepted 24 April 2014; Published 22 May 2014

Academic Editor: Her-Terng Yau

Copyright © 2014 Yung-Kuan Chan 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. K. Althoff, J. Degerman, and T. Gustavsson, “Combined segmentation and tracking of neural stem-cells,” in Image Analysis, vol. 3540 of Lecture Notes in Computer Science, pp. 282–291, 2005. View at Publisher · View at Google Scholar
  2. U. Sack, S. Knoechner, H. Warschkau, U. Pigla, F. Emmrich, and M. Kamprad, “Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics,” Autoimmunity Reviews, vol. 2, no. 5, pp. 298–304, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. Y.-L. Huang, Y.-L. Jao, T.-Y. Hsieh, and C.-W. Chung, “Adaptive automatic segmentation of HEp-2 cells in indirect immunofluorescence images,” in Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC '08), pp. 418–422, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. J. H. Price and D. A. Gough, “Comparison of phase-contrast and fluorescence digital autofocus for scanning microscopy,” Cytometry, vol. 16, no. 4, pp. 283–297, 1994. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Eddins, “The Watershed Transform: Strategies for Image Segmentation,” Newsletters—MATLAB News & Notes, February 2002, http://www.mathworks.com/company/newsletters/articles/the-watershed-transform-strategies-for-image-segmentation.html.
  6. C. Tang and B. Ewert, “Automatic tracking of neural sem cells,” in Proceedings of the APRS Workshop on Digital Image Computing (WDIC '05), pp. 61–66, Brisbane, Australia, February 2005.
  7. P. Yan, X. Zhou, M. Shah, and S. T. C. Wong, “Automatic segmentation of high-throughput RNAi fluorescent cellular images,” IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 1, pp. 109–117, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. Chaudry, “Cell Culture,” http://www.scq.ubc.ca/cell-culture/.
  9. S. Cooper, “A unifying model for the G1 period in prokaryotes and eukaryotes,” Nature, vol. 280, no. 5717, pp. 17–19, 1979. View at Publisher · View at Google Scholar · View at Scopus
  10. D. L. Olson and D. Delen, Advanced Data Mining Techniques, Springer, New York, NY, USA, 1st edition, 2008.
  11. N. Otsu, “A threshold selection method from gray-level histogram,” IEEE Trans Syst Man Cybern, vol. 9, no. 1, pp. 62–66, 1979. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Makhoul, K. Francis, S. Richard, and W. Ralph, “Performance measures for information extraction,” in The Proceedings of DARPA Broadcast News Workshop, pp. 249–252, Herndon, Va, USA, February 1999.
  13. A. Cumani, “Edge detection in multispectral images,” Graphical Models and Image Processing, vol. 53, no. 1, pp. 40–51, 1991. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  14. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, Upper Saddle River, NJ, USA, 2002.
  15. D. Hagyard, M. Razaz, and P. Atkin, “Analysis of watershed algorithms for grayscale images,” in Proceedings of the Processing of IEEE International Conferences on Image Processing, pp. 41–44, March 1996.
  16. K. Karantzalos and D. Argialas, “Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levellings,” International Journal of Remote Sensing, vol. 27, no. 24, pp. 5427–5434, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. C. L. Orbert, E. W. Bengtsson, and B. G. Nordin, “Watershed segmentation of binary images using distance transformations,” The Processing of SPIE: Nonlinear Image Processing IV, vol. 1902, pp. 159–170, 1993. View at Google Scholar
  18. H. Sun, J. Yang, and M. Ren, “A fast watershed algorithm based on chain code and its application in image segmentation,” Pattern Recognition Letters, vol. 26, no. 9, pp. 1266–1274, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. F. Meyer, “Topographic distance and watershed lines,” Signal Processing, vol. 38, no. 1, pp. 113–125, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  20. G. Borgefors, “Distance transformations in digital images,” Computer Vision, Graphics, & Image Processing, vol. 34, no. 3, pp. 344–371, 1986. View at Publisher · View at Google Scholar · View at Scopus
  21. S.-F. Yang-Mao, Y.-K. Chan, and Y.-P. Chu, “Edge enhancement nucleus and cytoplast contour detector of cervical smear images,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 38, no. 2, pp. 353–366, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. E. Davies, Machine Vision: Theory, Algorithms and Practicalities, chapter 5, Academic Press, 1990.
  23. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–168, 2004. View at Publisher · View at Google Scholar · View at Scopus