The Application of the Snake Model in Carcinoma Cell Image Segment

  • Zhen Zhang
  • Peng Zhang
  • Xiaobo Mao
  • Shanzhong Zhang
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)

Abstract

Accurate cell nucleus segmentation is crucial for the development of automated cytological cancer recognition and diagnosis system. The paper proposes an improved Snake model for esophageal cell image based on the study of several main methods for esophageal cell image and analysis of their advantages and disadvantages. The novel cell nucleus segmentation method has been tested on a number of cell images obtained from esophageal smear slide and the results are encouraging. Experimental results show that the presented method performs well on both well-separated nuclei and some overlapped nuclei.

Keywords

snake model image segmentation cell Image cell boundary  esophageal cancer 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kass, M., Witkin, A.P., Terzopoulo, S.D.: Snake: Active Contour Models. In: Process. First Int. Conf. Compute. Vision, London, pp. 259–268 (1987)Google Scholar
  2. 2.
    Kass, M., Witkin, A.P., Terzopoulos, S.D.: Snake: Active Contour Models. International journal on Computer Vision 1, 321–331 (1988)CrossRefGoogle Scholar
  3. 3.
    Xu, C., Prince, J.L.: Snake Shapes and Gradient Vector Flow. IEEE Trans Image Proc. 7(3), 359–369 (1998)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Fan, J., David, K.Y.: Automatic Image Segmentation by Integrating Edge Extraction and Seeded Region Growing. IEEE Trans On Image Process. 10, 1454–1466 (2001)MATHCrossRefGoogle Scholar
  5. 5.
    Dwi, A.: Cell Segmentation with Median Filter and Mathematical Morphology Operation. In: Proc. Of The IEEE 10th International Conference On Image Analysis And Processing, pp. 1034–1046 (1999)Google Scholar
  6. 6.
    Lam, K.M., Yan, H.: Fast Greedy Algorithm for Active Contours. Electronics Lett. 30, 21–22 (1994)CrossRefGoogle Scholar
  7. 7.
    Yi, Y.: The New Image Segmentation Based on the Snake Model. First Military Medical University. 32–67 (2005)Google Scholar
  8. 8.
    Xue, D.J., Ping, X.J.: The New Image Segmentation Based on Geomatics Morphology. Journal of Information Engineering University 4(4), 88–92 (2003)Google Scholar
  9. 9.
    Hang, D., Yu, R.: The Snake Model Uses in Edge Detection. Journal of Shanghai Jiaotong University 34, 848–850 (2000)Google Scholar
  10. 10.
    Zhang, Y.J.: Image Segmentation Appraisal Classification and Comparison. Journal of Image and Graphics 5A(1), 39–43 (2000)Google Scholar
  11. 11.
    Zhao, B.J., Li, D.: The Improvement Snake Model for Complex Edge Detection. Journal of Beijing Institute of Technology 24(2), 162–165 (2004)Google Scholar
  12. 12.
    Wang, Y.Q., Jia, Y.D.: One New Herat Nuclear Magnetic Resonance Image Division Method. Chinese Journal of Computers 1, 129–135 (2007)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Zhen Zhang
    • 1
  • Peng Zhang
    • 1
  • Xiaobo Mao
    • 1
  • Shanzhong Zhang
    • 2
  1. 1.School of Electrical EngineeringZhengzhou UniversityZhengzhouChina
  2. 2.Highway Administration Bureau of ZhengzhouZhengzhouChina

Personalised recommendations