Data Processing

We provide a set of tools for image processing, data analysis and data visualization. The diagram bellow illustrates the processing pipeline and what software tools can master the corresponding steps.



Number Crunchers

Image processing tends to use quite a bit of resources. Therefore we have a dedicated machine with lots of memory, enough disk space and nice graphic hardware that can be booked and used.


Campus Software

There is a collection of software that can be obtained from the  DIT.


Facility Software

We maintain licenses for the following specialized software packages:

  • Huygens (Scientific Volume Imaging) for confocal and wide-field deconvolution. It is installed on the Cosmos workstation.
  • Imaris (Bitplane) is especially useful for 3D (+time -> 4D) image processing. This software is installed on the Cosmos workstation. It is also available with a floatting licence for installation on your personal computer please contact us. We provide access to the following modules: Base(3D reconstruction), Filament Tracer (automatic neuron tracing), Measurement Pro (image statistics) and Vantage (data presentation)
  • StereoInvestigator (Microbrightfield) is used for sterological measurements. It is installed on our stereology microscope.


Open Source Software

There following open source projects are of special interest for microscopists. You can download and install them yourself, if you are an administrator on your computer, otherwise you can contact us.


ImageJ is the main framework we recommend and support for the processing of microscopy images. A large collection of plug-ins is available for download to extend its capabilities. Fiji (is just imageJ) is a distribution of ImageJ provided with a comprehensive set of plugins and up-to-date documentation. The NIH has a new ImageJ project running in development phase, but keep an eye on ImageJ2.


Python is a scripting language that is widly used in many domains. Some call it “glue-language”. However it is very popular in scientific computing, especially in bioinformatics but also increasingly in other scientific domains. A very good distribution that allows complex analysis and statistics and comes with nice visualization modules is the Anaconda distribution (free version available) that comes with a nice installer for mac and windows.
R is a project launched by our neighbors in the north and is an open-source implementation of S. It’s power lies in statistics (including all kinds of branches) and data visualization, It has a very convenient interface to download specific packages for particular techniques or packages that are designed to make it easier to handle a particular dataset. There is also a nice IDE (similar to the one of MATLAB), which goes under the name of RStudio.


KNIME is a project that was initiated by the machine learning community and has grown since into the branches of cheminformatics and life sciences. The source is written in Java, allowing platform independence but compared to Python or R it does not require any programming skills. It’s interface allows to build workflows similar to LabView or RapidMiner, where each node fulfills a processing step. Node based workflows have the advantage of facilitating complex processing tasks and allow (when  nicely arranged) for a more intuitive reading than scripts. Finally it is easily extensible on different levels of expertise: On a user level workflows can be reused. People who script can make other (non programmers) benefit from their efforts using the Scripting Integrations. Finally the plugin structure of java allows to implement custom nodes if needed.


CellProfiler is an image processing platform from the High Content Screening community. Thus it aims at robust batch processing of  many images. An Experiment can be processed by a pipeline, that is a sequence of processing steps reaching from pre-processing all the way to the image and object quantification.