An Automatic Segmentation Approach of Epithelial Cells Nuclei

  • Claudia Mazo
  • Maria Trujillo
  • Liliana Salazar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


Histology images are used to identify biological structures present in living organisms — cells, tissues, organs, and parts of organs. E-Learning systems can use images to aid teaching how morphological features relate to function and understanding which features are most diagnostic of organs. The structure of cells varies according to the type and function of the cell. Automatic cell segmentation is one of the challenging tasks in histology image processing. This problem has been addressed using morphological gradient, region-based methods and shape-based method approaches, among others. In this paper, automatic segmentation of nuclei of epithelial cells is addressed by including morphological information. Image segmentation is commonly evaluated in isolation. This is either done by observing results, via manual segmentation or via some other goodness measure that does not rely on ground truth images. Expert criteria along with images manually segmented are used to validate automatic segmentation results. Experimental results show that the proposed approach segments epithelial cells in a close way to expert manual segmentations. An average sensitivity of 76% and an average specificity of 77% were obtained on a selected set of images.


Cell Nucleus Segmentation Result Light Region Automatic Segmentation Structure Tensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Swank, P., Greenberg, S., Winkler, D., Hunter, N., Spjut, H., Estrada, R., Taylor, G.: Nuclear Segmentation of Bronchial Epithelial Cells by Minimax and Thresholding Techniques. A Comparison. Analytical and Quantitative Cytology 5(3), 153–158 (1983)Google Scholar
  2. 2.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Computer Society 8, 679–698 (1986)Google Scholar
  3. 3.
    Rao, A., Schunck, B.: Computing Oriented Texture Fields. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1989, pp. 61–68 (1989)Google Scholar
  4. 4.
    Pham, D.L., Xu, C., Prince, J.L.: A Survey of Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering, 315–338 (1998)Google Scholar
  5. 5.
    Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R.: Three-dimensional Multi-scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Images. Med. Image Anal. 2, 143–168 (1998)CrossRefGoogle Scholar
  6. 6.
    Frangi, A., Niessen, W., Hoogeveen, R., van Walsum, T., Viergever, M.: Model-based Quantitation of 3-D Magnetic Resonance Angiographic Images. IEEE Transactions on Medical Imaging 18(10), 946–956 (1999)CrossRefGoogle Scholar
  7. 7.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision. Chapman. Signal Processing 35, 102–104 (1999)Google Scholar
  8. 8.
    Nedzved, A., Ablameyko, S., Pitas, I.: Morphological Segmentation of Histology Cell Images. In: Pattern Recognition International Conference, vol. 1 (2000)Google Scholar
  9. 9.
    Rohr, K.: Landmark-based Image Analysis: Using Geometric and Intensity Models. In: Computational Imaging and Vision (2001)Google Scholar
  10. 10.
    Weickert, J.: A Scheme for Coherence-Enhancing Diffusion Filtering with Optimized Rotation Invariance. Journal of Visual Communication and Image Representation 13(1-2), 103–118 (2002)CrossRefGoogle Scholar
  11. 11.
    Tapas, K., David, M., Mount, N.S., Netanyahu, C.D., Piatko, R.S., Angela, Y.: An Efficient K-means Clustering Algorithm: Analysis and Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 881–892 (2002)CrossRefGoogle Scholar
  12. 12.
    Tsai, A., Yezzi Jr., A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A Shape-based Approach to the Segmentation of Medical Imagery Using Level Sets. IEEE Transactions on Medical Imaging 22(2), 137–154 (2003)CrossRefGoogle Scholar
  13. 13.
    Nosal, E.: Flood-fill Algorithms Used for Passive Acoustic Detection and Tracking. In: New Trends for Environmental Monitoring Using Passive Systems, pp. 1–5 (2008)Google Scholar
  14. 14.
    Dongju, L., Jian, Y.: Otsu Method and K-means. In: Ninth International Conference on Hybrid Intelligent Systems, HIS 2009, vol. 1, pp. 344–349 (2009)Google Scholar
  15. 15.
    Bibo, L., Chunli, M., Hui, W.: Pixel Level Image Fusion Based on Linear Structure Tensor. In: 2010 IEEE Youth Conference on Information Computing and Telecommunications (YC-ICT), pp. 303–306 (2010)Google Scholar
  16. 16.
    Gartner, L., Hiatt, J., Strum, J.: Cell Biology and Histology, 6th edn. Lippincott Williams & Wilkins (2010)Google Scholar
  17. 17.
    Harandi, N., Sadri, S., Moghaddam, N., Amirfattahi, R.: An Automated Method for Segmentation of Epithelial Cervical Cells in Images of ThinPrep. Journal of Medical Systems 34(6), 1043–1058 (2010)CrossRefGoogle Scholar
  18. 18.
    Barrientos, M., Madrid, H.: Normalized Cut Based Edge Detection. In: Proceedings of the Third Mexican Conference on Pattern Recognition, pp. 211–219 (2011)Google Scholar
  19. 19.
    Eramian, M., Daley, M., Neilson, D., Daley, T.: Segmentation of Epithelium in H&E Stained Odontogenic Cysts. Journal of Microscopy 244(3), 273–292 (2011)CrossRefGoogle Scholar
  20. 20.
    Deza, M.M., Elena, D.: Encyclopedia of Distances, pp. 94–95. Springer (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Claudia Mazo
    • 1
  • Maria Trujillo
    • 1
  • Liliana Salazar
    • 2
  1. 1.Escuela de Ingeniería de Sistemas y ComputaciónUniversidad del ValleCaliColombia
  2. 2.Departamento de MorfologíaUniversidad del ValleCaliColombia

Personalised recommendations