The KNIME Image Processing Extension adds new nodes to KNIME Analytics Platform to, e.g. read more than 100 different kinds of images (thanks to the Bio-Formats library), apply well-known methods for preprocessing, image segmentation, and classification. Most of the included nodes operate on multi-dimensional image data (e.g. videos, 3D images, multi-channel images or even a combination of them), which is made possible by ImgLib2. In addition, several nodes to calculate image features (e.g. zernike-, texture- or histogram features) for segmented images (e.g. a single cell) are included. These feature vectors can be used to apply machine learning methods in order to train and apply a classifier.
The KNIME Image Processing Extension currently provides about 90 nodes for (pre)processing, filtering, segmentation, feature extraction, various views, ....
For more information how to install/use KNIME Image Processing see: https://www.knime.com/community/image-processing
Example Workflows can be found at: https://hub.knime.com/knime/spaces/Examples/latest/99_Community/01_Image_Processing/
org.knime.knip.core
: Logic/Algorithms/DataStructures/Views independent of KNIMEorg.knime.knip.base
: KNIME Image Processing Nodes wrapping core and providing dedicated KNIME Image Processing functionality (NodeModels etc).org.knime.knip.feature
: Eclipse feature for org.knime.knip.core and org.knime.knip.baseorg.knime.knip.testing
: KNIME Image Processing Testing nodes, e.g. for regressions tests.org.knime.knip.testing.feature
: Eclipse feature for KNIME Image Processing Testings nodes.org.knime.knip.io
: Image Reader/Image Writer for KNIME Image Processing.org.knime.knip.update
: Eclipse update site for KNIME Image Processing and depedencies.org.knime.knip.tracking
: TrackMate Tracking integration.
See this repository for instructions.