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An Automated Method for Segmentation of Epithelial Cervical Cells in Images of ThinPrep

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Abstract

We present an automated method for segmentation of epithelial cells in images taken from ThinPrep scenes by a digital camera in a cytology lab. The method covers both steps of localization of cell objects in low resolution and detection of cytoplasm and nucleus boundary in high resolution. The underlying method makes use of geometric active contours as a powerful tool of segmentation. We also provide the analysis of the connected cells. For this purpose an automatic circular decomposition method is incorporated and adapted to the application by changing its segmentation condition. The results are evaluated numerically and compared with those of previous work in literature.

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References

  1. World Health Organization (February 2006). “Fact sheet No. 297: Cancer”. Retrieved on 2007-12-01.

  2. National Cancer Institute, NCI Women’s Health Report Fiscal Years 2005–2006, 2007.

  3. Bernstein, S. J., Sanchez-Ramos, L., and Ndubisi, B., Liquid-based cervical cytologic smear study and conventional Papanicolaou smears: a meta-analysis of prospective studies comparing cytologic diagnosis and sample adequacy. Am. J. Obstet. Gynecol. 185:308–317, 2001.

    Article  Google Scholar 

  4. Bamford, P., and Lovell, B., Unsupervised cell nucleus segmentation with active contours. Signal Processing Special Issue: Deformable Models and Technique for Image and Signal Processing 71( 2):203–213, 1998.

  5. Bamford, P., and Lovell, B., A water immersion algorithm for cytological image segmentation. In: APRS Image Segmentation Workshop, University of Technology Sydney, 75–79, 1996.

  6. Wu, H., Gil, J., and Barba, J., Optimal segmentation of cell images. Proc. IEEE Conf.-Vis. Image Signal Process. 145 (1)50–56, 1998.

    Article  Google Scholar 

  7. Walker, R. F., Adaptive Multi-Scale Texture Analysis with Application to Automated Cytology, Ph.D. thesis, Department of Electrical and Computer Engineering, University of Queensland, 1997.

  8. Martin, E., Pap-smear classification. M.S. thesis, Oersted-DTU, Technical University of Denmark, 2003.

  9. Norup, J., Classification of pap-smear data by transductive neuro-fuzzy methods, M.S. thesis, Oersted-DTU, Technical University of Denmark, 2005.

  10. Mao, S., Chan, Y., and Chu, Y., Edge enhancement nucleus and cytoplast contour detector of cervical smear images. IEEE Trans. SMC. 38 (2)353–366, 2008.

    Google Scholar 

  11. Xu, C., Pham, D. L., and Prince, J. L., Medical image segmentation using deformable models. In: Fitzpatrick, J. M., and Sonka, M. (Eds.), SPIE Handbook on Medical Imaging—Vol. 3: Medical Image Analysis, May 2000.

  12. Malladi, R., and Sethian, J. A., Level set methods for curvature flow, image enhancement and shape recovery in medical images. Proc of Conf. on Visualization and Mathematics, 1995, Berlin, Springer, Heidelberg, 329–345, 1997.

  13. Malladi, R., Sethian, J. A., and Vemuri, B. C., Shape modeling with front propagation: a level set approach. IEEE Trans. PAMI. 17:158–175, 1995.

    Google Scholar 

  14. Osher, S., and Fedkiw, R., Level Set Methods and Dynamic Implicit Surfaces. Springer, New York, pp. 64–69, 2003.

    MATH  Google Scholar 

  15. Adalsteinsson, D., and Sethian, J. A., A fast level set method for propagating interfaces. J. Comp. Phys. 118:269–277, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  16. Li, C., Xu, C., Gui, C., and Fox, M. D., Level set evolution without re-initialization: a new variational formulation. Proc. 2005 IEEE CVPR, 1:430–436, San Diego, 2005.

  17. Li, C., Xu, C., Gui, C., and Fox, M. D., Fast distance preserving level set evolution for medical image segmentation. Proc. 2006 IEEE ICARCV, 1–7, Singapore, 2006.

  18. Xu, C., and Prince, J. L., Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7 (3)359–369, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  19. Perona, P., and Malik, J., Scale-space and edge detection using anisotropic diffusion. IEEE Trans. PAMI. 12 (7)629–639, 1990.

    Google Scholar 

  20. Davies, E., Machine Vision: Theory, Algorithms and Practicalities. Academic, New York, 1990.

    Google Scholar 

  21. Canny, J., A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8 (6)679–698, 1986.

    Article  Google Scholar 

  22. Gonzalez, R., and Woods, R., Digital Image Processing. Prentice-Hall, New York, 2004.

    Google Scholar 

  23. Sauvola, J., and Pietikainen, M., Adaptive document image binarization. Pattern Recogn. 33 (2)225–236, 2000.

    Article  Google Scholar 

  24. Otsu, N., A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9 (1)62–66, 1979.

    Article  MathSciNet  Google Scholar 

  25. Shafaita, F., Keysers, D., and Breuel, T. M., Efficient implementation of local adaptive thresholding techniques using integral images. Proceedings of the 15th SPIE Conference on Document Recognition and Retrieval, 6815, 10–16, 2008.

  26. Duda, R. O., and Hart, P. E., Use of the Hough transformation to detect lines and curves in picture. Comm. ACM. 15:11–15, 1972.

    Article  Google Scholar 

  27. Guimaraes, L. V., Suzim A. A., and Maeda, J., A new automatic circular decomposition algorithm applied to blood cells image. Proceedings of IEEE International Symposium on Bioinformatics and Biomedical Engineering, 277–280, Arlington, VA, USA, 2000.

  28. Yasnoff, W. A., Mui, J. K., and Bacus, J. W., Error measures for scene segmentation. Pattern Recogn. 9:217–231, 1977.

    Article  Google Scholar 

  29. Sezgin, M., and Sankur, B., Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging. 13 (1)146–165, 2004.

    Article  Google Scholar 

  30. Zhang, Y. J., A survey on evaluation methods for image segmentation. Pattern Recogn. 29:1335–1346, 1996.

    Article  Google Scholar 

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Authors and Affiliations

  1. Digital Signal Processing Research Laboratory, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-8311, Iran

    Negar M. Harandi, Saeed Sadri & Rassul Amirfattahi

  2. Medical Image and Signal Processing Research Center, Isfahan University of Medical Science, Isfahan, 81746-73461, Iran

    Negar M. Harandi, Saeed Sadri, Noushin A. Moghaddam & Rassul Amirfattahi

Authors
  1. Negar M. Harandi
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  2. Saeed Sadri
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  3. Noushin A. Moghaddam
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  4. Rassul Amirfattahi
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Correspondence to Negar M. Harandi.

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Harandi, N.M., Sadri, S., Moghaddam, N.A. et al. An Automated Method for Segmentation of Epithelial Cervical Cells in Images of ThinPrep. J Med Syst 34, 1043–1058 (2010). https://doi.org/10.1007/s10916-009-9323-4

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  • DOI: https://doi.org/10.1007/s10916-009-9323-4

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