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Similarity analysis of cell movements in video microscopy

Scifer is a visualization software designed for the interactive analysis of scientific data.

Scifer currently supports the following application areas:

  • Biology: 3D video microscopy, TEM/SEM records of tissue
  • Climate research: time series analysis
  • Computational fluid dynamics: time series and feature analysis
  • Graph analysis
  • Multidimensional data analysis

This visualization method combines similarity measures of trajectory shapes and movement-relevant parameters to account for biological questions. In an interactive frame-work, the user can first cluster the cell trajectories according to problem-specific features and interactively validate and analyze the resulting clustering. The flexibility of the similarity metric constitutes a vital feature of this technique. It can be adjusted to describe a large variety of trajectory characteristics and may therefore be adapted to a large variety of task-specific demands. A second feature that proved highly useful is the extension of the visualization to 3D. While most existing techniques only plot the cell lineages in 2D, this method provides the user with different techniques to investigate their data in the natural 3D space.

Release Date:
Data type:
2D, 3D
Linux, Mac OSX
Linux (Ubuntu, Debian, OpenSuse), Mac OS X

Project development

Institution: Computer Graphics and Visualization Heidelberg University, Centre for Organismal Studies (COS) Heidelberg University

This work is supported by a grant of the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp).