Gradients and Active Contour Models for Localization of Cell Membrane in HER2/neu Images

  • Marek WdowiakEmail author
  • Tomasz Markiewicz
  • Stanislaw Osowski
  • Janusz Patera
  • Wojciech Kozlowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)


The paper presents an application of the snake model to recognition of the cell membrane in the HER2 breast and kidney cancer images. It applies the modified snake to build the system recognizing the membrane and associating it with the neighboring cell. We study different forms of gradient estimation, the core point in the snake model. The particle swarm optimization algorithm is used in tuning the parameters of the snake model. On the basis of the applied procedure the estimation of the membrane continuity of cell is made. The experimental results performed on 100 cells in breast and 100 cells in kidney cancers have shown high accuracy of the membrane localizations and acceptable agreement with the expert estimations.


Image segmentation Object recognition HER2/neu Snake 


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  1. 1.
    PATHWAY HER-2/neu (4B5) – user manual. Ventana Medical Systems, Tucson (2009)Google Scholar
  2. 2.
    Matlab Image Processing Toolbox: users guide. MathWorks, Natick (2012)Google Scholar
  3. 3.
    Grala, B., Markiewicz, T., Kozlowski, W., Osowski, S., Slodkowska, J., Papierz, W.: New automated image analysis method for the assessment of ki-67 labeling index in meningiomas. Folia Histo. Cyto. 47(4), 587–592 (2009)Google Scholar
  4. 4.
    Kasson, P., Huppa, J., Davis, M., Brunger, A.: A hybrid machine-learning ap-proach for segmentation of protein localization data. Bioinformatics 2(19), 3778–3786 (2005)CrossRefGoogle Scholar
  5. 5.
    Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  6. 6.
    Latif, Z., Watters, A., Bartlet, J., Underwood, M., Aichison, M.: Gene amplification and overexpression of her2 in renal cell carcinoma. BJU Intern. 89, 5–9 (2002)CrossRefGoogle Scholar
  7. 7.
    Les, T., Markiewicz, T., Osowski, S., Cichowicz, M., Kozlowski, W.: Automatic evaluation system of FISH images in breast cancer. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2014. LNCS, vol. 8509, pp. 332–339. Springer, Heidelberg (2014) Google Scholar
  8. 8.
    Lezoray, O., Elmoataz, A., Cardot, H., Gougeon, G., Lecluse, M., Elie, H., Revenu, M.: Segmentation of colour images from serous cytology for automated cell classification. Anal. Quant. Cytol. Histol. 22, 311–322 (2000)Google Scholar
  9. 9.
    Littlejohns, P.: Trastuzumab for early breast cancer: evolution or revolution? Lancet Oncology 7(1), 22–33 (2006)CrossRefGoogle Scholar
  10. 10.
    Logan, J., Edwards, K., Saunders, N.: Real-Time PCR: Current Technology and Applications. Caister Academic Press, Norfolk (2009) Google Scholar
  11. 11.
    Naegel, B., Passat, N., Ronse, C.: Grey-level hit-or-miss transforms part i:unified theory. Pattern Recogn. 40, 635–647 (2007)CrossRefzbMATHGoogle Scholar
  12. 12.
    Soille, P.: Morphological Image Analysis, Principles and Applications, 2nd edn. Springer, Berlin (2003) zbMATHGoogle Scholar
  13. 13.
    Tabakov, M., Kozak, P.: Segmentation of histopathology her2/neu images with fuzzy decision tree and takagi-sugeno reasoning. Comput. Biol. Med. 49, 19–29 (2014)CrossRefGoogle Scholar
  14. 14.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to data mining. Pearson Education Inc., Boston (2006) Google Scholar
  15. 15.
    Wdowiak, M., Markiewicz, T., Osowski, S., Swiderska, Z., Patera, J., Kozlowski, W.: Hourglass shapes in rank grey-level hit-or-miss transform for membrane segmentation in HER2/neu images. In: Benediktsson, J.A., Chanussot, J., Najman, L., Talbot, H. (eds.) Mathematical Morphology and Its Applications to Signal and Image Processing. LNCS, vol. 9082, pp. 3–14. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  16. 16.
    Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Processing 7(3), 359–369 (1998)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marek Wdowiak
    • 1
    Email author
  • Tomasz Markiewicz
    • 1
    • 2
  • Stanislaw Osowski
    • 1
    • 3
  • Janusz Patera
    • 2
  • Wojciech Kozlowski
    • 2
  1. 1.Warsaw University of TechnologyWarsawPoland
  2. 2.Military Institute of MedicineWarsawPoland
  3. 3.Military University of TechnologyWarsawPoland

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