A Narrow-Band Level-Set Method with Dynamic Velocity for Neural Stem Cell Cluster Segmentation

  • Nezamoddin N. Kachouie
  • Paul Fieguth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3656)


Neural Stem Cells (NSCs) have a remarkable capacity to proliferate and differentiate to other cell types. This ability to differentiate to desirable phenotypes has motivated clinical interests, hence the interest here to segment Neural Stem Cell (NSC) clusters to locate the NSC clusters over time in a sequence of frames, and in turn to perform NSC cluster motion analysis. However the manual segmentation of such data is a tedious task. Thus, due to the increasing amount of cell data being collected, automated cell segmentation methods are highly desired. In this paper a novel level set based segmentation method is proposed to accomplish this segmentation. The method is initialization insensitive, making it an appropriate solution for automated segmentation systems. The proposed segmentation method has been successfully applied to NSC cluster segmentation.


Neural Stem Cell Velocity Function Cell Segmentation Dynamic Velocity Gray Level Intensity 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nistor, G., Totoiu, M., Haque, N., Carpenter, M., Keirstead, H.: Human em- bryonic stem cells differentiate into oligodendrocytes in high purity and myelinate after spinal cord transplantation. GLIA 49(3), 385–396 (2004)CrossRefGoogle Scholar
  2. 2.
    Wu, K., Gauthier, D., Levine, M.: Live cell image segmentation. IEEE Trans- actions on Biomedical Engineering 42(1), 1–12 (1995)CrossRefGoogle Scholar
  3. 3.
    Geusebroek, J., Smeulders, A., Cornelissen, F.: Segmentation of cell clusters by nearest neighbour graphs. In: Proceedings of the third annual conference of the Advanced School for Computing and Imaging, pp. 248–252 (1997)Google Scholar
  4. 4.
    Markiewicz, T., Osowski, S., Moszczyski, L., Satat1, R.: Myelogenous leukemia cell image preprocessing for feature generation. In: 5th International Workshop on Computational Methods in Electrical Engineering, pp. 70–73 (2003)Google Scholar
  5. 5.
    Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. In: Advanced algorithmic approaches to medical image segmentation: State-of-the-art applications in cardiology, neurology, mammography and pathology, pp. 541–558 (2001)Google Scholar
  6. 6.
    Osher, S.J., Fedkiw, R.P.: Level Set Methods and Dynamic Implicit Surfaces. Springer, Heidelberg (2002)Google Scholar
  7. 7.
    Sethian, J.: Level Set Methods and Fast Marching Methods Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision and Materials Science. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  8. 8.
    Yezzi, J.A., Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A.: A geo- metric snake model for segmentation of medical imagery. IEEE Tran. on Medical Imaging 16(2), 199–209 (1997)CrossRefGoogle Scholar
  9. 9.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front prop- agation: A level set approach. IEEE Transactions on PAMI 27(2), 158–175 (1995)Google Scholar
  10. 10.
    Donoho, D.L., Johnstone, I.M.: Denoising by soft thresholding. IEEE Tran. on Inf. Theory 41, 613–627 (1997)CrossRefGoogle Scholar
  11. 11.
    Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. on Image Processing 9(9), 1532–1546 (2000)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Nezamoddin N. Kachouie
    • 1
  • Paul Fieguth
    • 1
  1. 1.Department of Systems Design EngineeringUniversity of WaterlooWaterlooCanada

Personalised recommendations