Image Processing and Reconstruction of Cultured Neuron Skeletons

  • Donggang Yu
  • Tuan D. Pham
  • Jesse S. Jin
  • Suhuai Luo
  • Hong Yan
  • Denis I. Crane
Part of the Studies in Computational Intelligence book series (SCI, volume 450)


One approach to investigating neural death is through systematic studies of the changing morphology of cultured brain neurons in response to cellular challenges. Image segmentation and neuron skeleton reconstruction methods developed to date to analyze such changes have been limited by the low contrast of cells. In this paper we present new algorithms that successfully circumvent these problems. The binary method is based on logical analysis of grey and distance difference of images. The spurious regions are detected and removed through use of a hierarchical window filter. The skeletons of binary cell images are extracted. The extension direction and connection points of broken cell skeletons are automatically determined, and broken neural skeletons are reconstructed. The spurious strokes are deleted based on cell prior knowledge. The reconstructed skeletons are processed furthermore by filling holes, smoothing and extracting new skeletons. The final constructed neuron skeletons are analyzed and calculated to find the length and morphology of skeleton branches automatically. The efficacy of the developed algorithms is demonstrated here through a test of cultured brain neurons from newborn mice.


Neuron cell image image segmentation grey and distance difference filtering window neuron skeleton skeleton reconstruction skeleton branch 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allani, P.K., Sum, T., Bhansali, S.G., Mukherjee, S.K., Sonee, M.: A comparative study of the effect of oxidative stress on the cytoskeleton in human cortical neurons. Toxicol. Appl. Pharmacol. 196, 29–36 (2004)CrossRefGoogle Scholar
  2. 2.
    Andersen, J.: Oxidative stress in neurodegeneration: cause or consequence. Nat. Med. 10(suppl. S), 18–25 (2004)CrossRefGoogle Scholar
  3. 3.
    Ischiropoulos, B., Ischiropoulos, H., Beckman, J.S.: Oxidative stress and nitration in neurodegeneration: Cause, effect, or association. J. Clin. Invest. 111, 163–169 (2003)Google Scholar
  4. 4.
    Smit, M., Leng, J., Klemke, J.R.: Assay for neurite outgrowth quantification. Biotechniques 35(2), 254–256 (2003)Google Scholar
  5. 5.
    Xiong, G., Zhou, X., Degterev, A., Ji, L., Wong, S.T.C.: Automated neurite labeling and analysis in fluorescence microscopy images. Cytometry A 69A, 494–505 (2006)CrossRefGoogle Scholar
  6. 6.
    Meijering, E., Jacob, M., Sarria, J.-C.F., Steiner, P., Hirling, H., Unser, M.: Neurite Tracing in Fluorescence Microscopy Images using Ridge Filtering and Graph Searching: Principles and Validation. In: Leahy, R., Roux, C. (eds.) Proceedings of the 2004 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1219–1222. IEEE, Piscataway (2004)Google Scholar
  7. 7.
    Otsu, N.: A thresholding selection method from greylevel histogram. IEEE Trans. Systems Man Cybernet. 8, 62–66 (1978)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Lee, S.U., Chung, S.Y., Park, R.H.: A comparative performance study of several global thresholding techniques for segmentation. CVGIP 52, 171–190 (1990)Google Scholar
  9. 9.
    Chi, Z., Yan, H., Pham, T.: Fuzzy Algorithm: With Application to Image Processing and Pattern Recognition. World Scientific Publishing Co., Singapore (1996)CrossRefGoogle Scholar
  10. 10.
    Papamarkos, N., Gatos, B.: A new approach for multilevel threshold selection. CVGIP: Graphical Models Image Process. 56, 357–370 (1998)CrossRefGoogle Scholar
  11. 11.
    Rosin, P.L.: Unimodal thresholding. Pattern Recognition 34, 2083–2096 (2001)MATHCrossRefGoogle Scholar
  12. 12.
    Smit, M., Leng, J., Klemke, R.: Assay for neurite outgrowth quantification. Biotechniques 35, 254–256 (2003)Google Scholar
  13. 13.
    Zhang, Y., Zhou, X., Wong, S.T.C.: Extraction of Neurite Structures for High Throughput Imaging Screening of Neuron Based Assays. In: Proced. IEEE/NLM Life Science Systems and Applications Workshop, IEEE/NLM, pp. 38–39 (2006)Google Scholar
  14. 14.
    Pal, U., Rodenacker, K., Chaudhuri, B.B.: Automatic cell segmentation in Cyto- and Histometry using dominant contour feature points. Journal of the European Society for Analytical Cellular Pathology 17, 243–250 (1998)Google Scholar
  15. 15.
    Wu, H.S., Barba, J., Gil, J.: A parametric fitting algorithm for segmentation of cell images. IEEE Trans. Biomed. Eng. 45(3), 400–407 (1998)CrossRefGoogle Scholar
  16. 16.
    Pham, T.D., Crane, D.I.: Segmentation of neuronal-cell images from stained fields and monomodal histograms. In: Proc. 27th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, vol. 3.5, pp. 7–13 (2005)Google Scholar
  17. 17.
    Deravi, F., Pal, S.K.: Grey level thresholding using second order statistics. Pattern Recognition Lett. 1, 417–422 (1963)CrossRefGoogle Scholar
  18. 18.
    Nakagawa, Y., Rosenfeld, A.: Some experiments on variable thresholding. Pattern Recognition 11, 191–204 (1979)CrossRefGoogle Scholar
  19. 19.
    Boukharouba, S., Rebordao, J.M., Wendel, P.L.: An amplitude segmentation method based on the distribution function of an image. Computer Vision Graphics Image Process. 29, 47–59 (1985)CrossRefGoogle Scholar
  20. 20.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, New Jersey (2002)Google Scholar
  21. 21.
    Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for grey-level picture thresholding using the entropy of the histogram, Comput. Vision Graphics Image Process. 29, 273–285 (1985)CrossRefGoogle Scholar
  22. 22.
    Glasbey, C.A.: An analysis of histogram-based thresholding algorithms A. CVGIP: Graphical Models and Image Processing 55, 532–537 (1993)CrossRefGoogle Scholar
  23. 23.
    Kamel, M., Zhao, A.: Extraction of binary character graphics images from greyscale document images. CVGIP: Graphical Models Image Process. 55, 203–217 (1993)CrossRefGoogle Scholar
  24. 24.
    Yang, Y., Yan, H.: An adaptive logical method for binarization of degraded document images. Pattern Recognition 33, 787–807 (2000)CrossRefGoogle Scholar
  25. 25.
    Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Communications of ACM 27, 236–239 (1984)CrossRefGoogle Scholar
  26. 26.
    Yu, D., Yan, H.: An efficient algorithm for smoothing, linearization and detection of structure feature points of binary image contours. Patt. Recog. 30, 57–69 (1997)CrossRefGoogle Scholar
  27. 27.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Donggang Yu
    • 1
    • 2
  • Tuan D. Pham
    • 1
  • Jesse S. Jin
    • 2
  • Suhuai Luo
    • 2
  • Hong Yan
    • 3
    • 4
  • Denis I. Crane
    • 5
    • 6
  1. 1.ADFA School of Information Technology and Electrical EngineeringThe University of New South WalesCanberraAustralia
  2. 2.School of Desigh, Communication and Information TechnologyThe University of NewcastleCallaghanAustralia
  3. 3.Department of Electronic EngineeringCity University of Hong KongKowloonHong Kong
  4. 4.School of Electrical and Information EngineeringUniversity of SydneySydneyAustralia
  5. 5.School of Biomolecular and Biomedical ScienceGriffith UniversityNathanAustralia
  6. 6.Eskitis Institute for Cell and Molecular TherapiesGriffith UniversityNathanAustralia

Personalised recommendations