Special Interest Group "High Content Analytics" (SIG-HCA)

Community Contributions

The following contributions from the open source community are currently bundled within the update site available at:

http://labs.knime.org/sig_hca/update

The update site allows to install the following plugins: KNIME Image Processing and Mining

• The base image processing nodes from Konstanz University encapsulating:
  - Reader and Writer nodes for various formats (based on the BioFormats library) - simple preprocessing nodes to dissect and combine image stacks - exemplary nodes for further image processing (like thresholding, binary operations, inverting, ...) - a node to execute ImageJ-macros - a basic View Node to browse through image stacks, show them as RGB-images or display their histograms
• The image mining nodes from Konstanz University including:
  - two segmentation nodes (connected component analysis and a region growing algorithm) - some nodes for feature calculation (texture and histogram features) - a node providing the hilite-mechanism for image segments

Note that the source code is included in the plug-in archive (you can find it after updating in the plugin directory of your KNIME-installation: org.knime.ext.image_***.jar). A demo workflow (including cell images) demonstrating the image processing and mining nodes by means of a cell image segmentation and classification task can be downloaded here (281 KB).
HC/DC The HC/DCnodes which have been developed by the Data Handling Unit of High Content Screening Facility – RISC, at the ETH Zurich. High Content Screening (HCS) is increasingly used for the automated evaluation of spatio-temporally resolved multiple biochemical and morphological parameters in cellular systems. HCS data include comprehensive information about the bioactive molecules, the targeted genes, and images as well as their extracted data matrices after acquisition. The architecture of HC/DC is based mostly on the KNIME platform and Eclipse plug-in framework. HC/DC is a functional set of nodes for HCS, working together with KNIME and imageJ. A plug-in for opening and processing proprietary HCS files (library management, numeric results and images) was developed within the KNIME environment. All those open source components (Eclipse environment, KNIME, R-Project, Weka and ImageJ) were chosen for its platform-independence, openness, simplicity, and portability. They are also the fastest pure Java image-, data-processing programs currently available. Next steps: large scale biological data management, high content screening library handling, support image based experiments, results handling, data mining, screening plate based data visualization.