Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images

  • M. Tscherepanow
  • F. Zöllner
  • M. Hillebrand
  • F. Kummert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5108)


The automatic subcellular localisation of proteins in living cells is a critical step in determining their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous to recognise these cells in bright-field microscope images taken in parallel with the regarded fluorescence micrographs. Unfortunately, currently available cell recognition methods are only of limited use within the context of protein localisation, since they frequently require microscopy techniques that enable images of higher contrast (e.g. phase contrast microscopy or additional dyes) or can only be employed with too low magnifications. Therefore, this article introduces a novel approach to the robust automatic recognition of unstained living cells in bright-field microscope images. Here, the focus is on the automatic segmentation of cells.


Active Contour Automatic Segmentation Cell Recognition Cell Segmentation Algebraic Opening 
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 2008

Authors and Affiliations

  • M. Tscherepanow
    • 1
  • F. Zöllner
    • 2
  • M. Hillebrand
    • 1
  • F. Kummert
    • 1
  1. 1.Applied Computer Science, Faculty of TechnologyBielefeld UniversityBielefeldGermany
  2. 2.Computer Assisted Clinical Medicine, Faculty of Medicine MannheimUniversity of HeidelbergMannheimGermany

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