Cell Nuclei Counting and Segmentation for Histological Image Analysis

  • Maryna LukashevichEmail author
  • Valery Starovoitov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1055)


The paper describes our experiments of automated digital histological image analysis. In our study we investigated two tasks. The primary goal was to develop a simple and effective automated scheme of cell nuclei counting. The second goal was to demonstrate that for histological images with homogeneous background we can apply a binarization technique and approximately calculate the number of nuclei in the image. The experiments were done on two public datasets of histological images. The experiments have demonstrated acceptable level of calculation results.


Histological analysis Image analysis Cell nuclei counting Nuclei segmentation 


  1. 1.
    Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review current status and future potential. IEEE Rev. Biomed. Eng. 7, 97–114 (2014)CrossRefGoogle Scholar
  2. 2.
    Chen, S., Zhao, M., Wu, G., Yao, C., Zhang, J.: Recent advances in morphological cell image analysis. Hindawi Publishing (2012). Corporation: Computational and Mathematical Methoda in MedicineGoogle Scholar
  3. 3.
    Jung, C., Kim, C.: Impact of the accuracy of automatic segmentation of cell nuclei clusters on classification of cell nuclei clusters on classification of thyroid follicular lesions. Cytometry Part A 85A, 709–719 (2014)CrossRefGoogle Scholar
  4. 4.
    Saharma, H., et al.: A multi-resolution approach for combining visual information using nuclei segmentation and classification in histopathological images. In: Proceedings of the 10th International Conference on Computer Vision, Theory and Applications (VISAPP 2015), pp. 37–46 (2015)Google Scholar
  5. 5.
    Alilou, M., Kovalev, V., Taimouri, V.: Segmentation of cell nuclei in heterogeneous microscopy images: a reshapable templates approach. Comput. Med. Imaging Graph. 37, 488–499 (2013)CrossRefGoogle Scholar
  6. 6.
    Kowal, M., Filipczuk, P.: Nuclei segmentation for computer-aided diagnosis of breast cancer. Int. J. Appl. Math. Comput. Sci. 24(1), 19–31 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wienert, S., Helm, D., Saeger, K., Stenziger, A., Beil, M., Hufnagl, P., et al.: Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Nat. Sci. Rep. 2, 503 p. (2012)Google Scholar
  8. 8.
    Zang, C., et al.: White blood cell segmentation by color-space-based K-means clustering. Sensors 14, 16128–16147 (2014). Scholar
  9. 9.
    Song, Y., Cai, W., Huang, H., Wang, Y., Feng, D.D., Chen, M.: Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling. BMC Bioinf. 14, 173 p. (2013)Google Scholar
  10. 10.
    Coelho, L.P., Shariff, A., Murphy, R.F.: Nuclear segmentation in microscope cell images: a hand-segmented dataset and comparison of algorithms. In: Proceedings of IEEE International Symposium Biomedical Imaging, pp. 518–521 (2009)Google Scholar
  11. 11.
    Signolle, N., Revenu, M., Plancoulaine, B., Herlin, P.: Wavelet-based multiscale texture segmentation in application to stromal compartment characterization on virtual slides. Signal Process. 90(8), 2412–2422 (2010)CrossRefGoogle Scholar
  12. 12.
    Lezoray, O., et al.: Segmentation of cytological image using color and mathematical morphology. Acta Stereologica 18, 1–14 (1999)Google Scholar
  13. 13.
    Loukas, C.G., Wilson, G.D., Vojnovic, B., Linney, A.: an image analysis-based approach for automated counting of cancer cell nuclei tissue sections. Cytometry Part A 55A, 30–42 (2003)CrossRefGoogle Scholar
  14. 14.
    Al-Kofahi, Y., Lassoued, W., Grama, K., Nath, S.K., Zhu, J., Oueslati, R., et al.: Cell-basedquantification of molecular biomarkers in histopathology specimens. Histopathology 59(1), 40–54 (2011)CrossRefGoogle Scholar
  15. 15.
    Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via L0 gradient minimization. ACM Trans. Graph. 30(6). Article 174 (2011)Google Scholar
  16. 16.
    Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recogn. 33, 225–236 (2000)CrossRefGoogle Scholar
  17. 17.
    Shafait, F., Keysers, D., Breuel, T.M.: Efficient implementation of local adaptive thresholding techniques using integral images. In: Document Recognition and Retrieval XV (2008)Google Scholar
  18. 18.
    Stathis, P., Kavallieratou, E., Papamarkos, N.: An evaluation technique for binarization algorithms. J. Univ. Comput. Sci. 14(18), 3011–3030 (2008)Google Scholar
  19. 19.
    Data Science Bowl. Accessed 29 Apr 2018
  20. 20.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. Accessed 29 Apr 2018

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Belarusian State University of Informatics and RadioelectronicsMinskRepublic of Belarus
  2. 2.The United Institute of Informatics ProblemMinskRepublic of Belarus

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