Selected Applications of Graph-Based Tracking Methods for Cancer Research

  • Pascal VallottonEmail author
  • Lilian Soon
Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)


Cell appearance is highly variable, particularly for cancer cells. Consequently, massive amounts of image data need to be screened rapidly and reproducibly to ascertain phenotypic information of interest. This demands a high level of automation and chosen image analysis techniques.A very relevant phenotype for cancer is motile behaviour as it is a fundamental ingredient of its development. We aim to measure normal and abnormal motile processes, identify small molecules and genotypes that modulate these processes, and develop mathematical models of the disease towards identifying specific targets or target sets. Here, we describe the use and principles underlying representative software tools to read motile phenotypes from time-lapse data, with emphasis on improved graph-based tracking methods.


Object Tracking Cell Tracking Manual Tracking Bipartite Match Cell Front 
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 Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.CSIRO Mathematical and Information SciencesNorth RydeAustralia

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