An Automatic Cell Counting Method for a Microscopic Tissue Image from Breast Cancer

  • Pornchai Phukpattaranont
  • Pleumjit Boonyaphiphat
Part of the IFMBE Proceedings book series (IFMBE, volume 15)


This paper presents an automatic cell counting method for a microscopic tissue image from breast cancer. We perform color space changing from RGB to CIELab and anisotropic diffusion filtering for noise removal in the preprocessing stage. Subsequently, the segmentation algorithm based on local adaptive thresholding, morphological operations, and cell size considerations is performed. In order to obtain the more correct counting number of cancer cells, we further process the image containing attached cancer cells with marker-controlled watershed segmentation. Results from our automatic counting approach show a promising solution to the traditional manual analysis. That is, the counting number of cancer cells from the automatic approach is comparable to that from a specialist.


Quantitative immunohistopathology Image segmentation Cancer cell images 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Thiran J, Macq B (1996) Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Transactions on biomedical engineering 43(10):1011–1020CrossRefPubMedGoogle Scholar
  2. 2.
    Fang B, Hsu W, Lee M (2003) On the accurate counting of tumor cells. IEEE Transactions on nanobioscience 2(2): 94–103CrossRefPubMedGoogle Scholar
  3. 3.
    Zhao P, Mao K, Koh T, Tan P (2003) Automatic cell analysis for P53 immunohistochemistry in bladder inverted papilloma. IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, 2003, pp 168–169.Google Scholar
  4. 4.
    Petushi S, Katsinis C, Coward C et al (2004) Automated identification of microstructures on histology slides, IEEE International Symposium on Biomedical Imaging: Macro to Nano vol. 1, 2004, pp 424–427.Google Scholar
  5. 5.
    O’Gorman L, Sanderson A, Preston K Jr (1985) A system for automated liver tissue image analysis: Methods and results. IEEE Transactions on biomedical engineering 32(9):696–706CrossRefPubMedGoogle Scholar
  6. 6.
    Wu K, Gauthier D, Levine M (1995) Live cell image segmentation. IEEE Transactions on biomedical engineering 42(1):1–12CrossRefPubMedGoogle Scholar
  7. 7.
    Phukpattaranont P, Boonyaphiphat P (2006) Automatic classification of cancer cells in microscopic images: Preliminary results, The 2006 ITC-CSCC International Conference vol. 1, Chiang Mai, Thailand, 2006, pp 113–116Google Scholar
  8. 8.
    Phukpattaranont P, Boonyaphiphat P et al (2006) Segmentation of cancerous cell image using local adaptive thresholding and morphological operators, The 2nd Regional Conference on Artificial Life and Robotics, Songkhla, Thailand, 2006, pp 68–71Google Scholar
  9. 9.
    Trussell H, Saber E, Vrhel M (2005) Color image processing (basics and special issue overview). IEEE signal processing magazine 22(1):14–22CrossRefGoogle Scholar
  10. 10.
    Perona P and Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence 12(7):629–639CrossRefGoogle Scholar
  11. 11.
    Otsu N (1979) A threshold selection method from graylevel histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1):62–66CrossRefGoogle Scholar
  12. 12.
    Vincent L (1993) Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Transactions on Image Processing 2(2):176–201CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Pornchai Phukpattaranont
    • 1
    • 2
  • Pleumjit Boonyaphiphat
    • 3
    • 2
  1. 1.Prince of Songkla UniversitySongkhlaThailand
  2. 2.Department of Electrical EngineeringPrince of Songkla UniversitySongkhlaThailand
  3. 3.Department of PathologyPrince of Songkla UniversitySongkhlaThailand

Personalised recommendations