KNIME
 | |
|---|---|
| Author | KNIME team |
| Maintainer | KNIME team |
| Source | on GitHub |
| Development status | active |
| Website | http://tech.knime.org/community/image-processing |
KNIME, the Konstanz Information Miner, is an open source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. A graphical user interface allows assembly of nodes for data preprocessing (ETL: Extraction, Transformation, Loading), for modeling and data analysis and visualization.
The KNIME Image Processing extension, KNIP, provides ca. 100 nodes for (pre)-processing, filtering, segmentation, feature extraction, various views (2D, 3D), etc. and integrations for various other image processing tools are available.
As part of the KNIP extension, there is an ImageJ extension for KNIME consisting of two basic parts: the support for ImageJ1 macro execution and the integration of ImageJ2 plugins as KNIME nodes. Both approaches are available as KNIME plugin (currently beta status).
See also
- The ImageJ Ops framework for image-processing algorithms, which is developed as a collaboration between the ImageJ and KNIME teams.
Publications
- Fillbrunn, A.; Dietz, C. & Pfeuffer, J. et al. (2017), "KNIME for reproducible cross-domain analysis of life science data", Journal of Biotechnology 261: 149-156, PMID 28757290, doi:10.1016/j.jbiotec.2017.07.028, <https://www.sciencedirect.com/science/article/pii/S0168165617315651> (on Google Scholar).
- Dietz, C. & Berthold, M. R. (2016), "KNIME for Open-Source Bioimage Analysis: A Tutorial", Focus on Bio-Image Informatics, Springer International Publishing, pp. 179-197, PMID 27207367, doi:10.1007/978-3-319-28549-8_7, <http://link.springer.com/chapter/10.1007/978-3-319-28549-8_7> (on Google Scholar).
- Berthold, M. R.; Cebron, N. & Dill, F. et al. (2007), "KNIME: The Konstanz Information Miner", Studies in Classification, Data Analysis, and Knowledge Organization (GfKL), Springer
