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Combined Segmentation and Tracking of Neural Stem-Cells

  • K. Althoff
  • J. Degerman
  • T. Gustavsson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

In this paper we analyze neural stem/progenitor cells in an time-lapse image sequence. By using information about the previous positions of the cells, we are able to make a better selection of possible cells out of a collection of blob-like objects. As a blob detector we use Laplacian of Gaussian (LoG) filters at multiple scales, and the cell contours of the selected cells are segmented using dynamic programming. After the segmentation process the cells are tracked in the sequence using a combined nearest-neighbor and correlation matching technique. An evaluation of the system show that 95% of the cells were correctly segmented and tracked between consecutive frames.

Keywords

Active Contour Consecutive Frame Assignment Weight Image Border Cell Contour 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • K. Althoff
    • 1
  • J. Degerman
    • 1
  • T. Gustavsson
    • 1
  1. 1.Department of Signals and SystemsChalmers University of TechnologyGothenburgSweden

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