Cell migration analysis: Segmenting scratch assay images with level sets and support vector machines

Authors:
Markus Glaí;Birgit MöLler;Anne Zirkel;Kristin WäChter;Stefan HüTtelmaier;Stefan Posch
Affiliations:
Zentrum für Angewandte Medizinische und Humanbiologische Forschung (ZAMED), Martin Luther University Halle-Wittenberg, Heinrich-Damerow-Str. 1, 06120 Halle (Saale), Germany;Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06099 Halle (Saale), Germany;Zentrum für Angewandte Medizinische und Humanbiologische Forschung (ZAMED), Martin Luther University Halle-Wittenberg, Heinrich-Damerow-Str. 1, 06120 Halle (Saale), Germany;Zentrum für Angewandte Medizinische und Humanbiologische Forschung (ZAMED), Martin Luther University Halle-Wittenberg, Heinrich-Damerow-Str. 1, 06120 Halle (Saale), Germany;Zentrum für Angewandte Medizinische und Humanbiologische Forschung (ZAMED), Martin Luther University Halle-Wittenberg, Heinrich-Damerow-Str. 1, 06120 Halle (Saale), Germany;Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06099 Halle (Saale), Germany
Venue:
Pattern Recognition
Year:
2012

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Abstract

Cell migration assessment is often done by scratch assay experiments for which quantitative evaluations are usually performed manually. Here we present an automatic analysis pipeline detecting scratch boundaries and measuring areas based on level sets. We extend non-PDE level sets for topology-preservation and use an entropy-based energy functional. This approach by design segments a scratch in every image, hence, we employ support vector machines to identify images showing no scratch at all. Compared to other algorithms our approach, implemented as ImageJ plugin, relies on a minimal set of parameters. Experimental evaluations show the high quality of results and their suitability for biomedical investigations.