Image feature detection

An often used Laplacian filter for enhancing signals at object boundaries and dots. It works with XY, XYZ, XYZ-T, XYZ-T-Ch1, XYZT-C1-C2 images. Distributed as a part of ImageJ plugin FeatureJ, and included in Fiji. The second URL above is the link to its Javadoc. (imagescience.feature.Laplacian). A primer for using this class in Jython script is in CMCI Jython/Fiji cookbook: FeatureJ.

This plugin will return on a full 256-grey level image (limitation in this version) or on a ROI several texture features such as described in Haralick publication. Can be run in batch mode.

This workflow classifies, or segments, the pixels of an image given user annotations. It is especially suited if the objects of interests are visually (brightness, color, texture) distinct from their surrounding. Users can iteratively select pixel features and provide pixel annotations through a live visualization of selected feature values and current prediction responses. Upon users' satisfaction, the workflow then predicts the remaining unprocessed image(s) regions or new images (as batch processing). Users can export (as images of various formats): selected features, annotations, predicted classification probability, simple segmentation, etc. This workflow is often served as one of the first step options for other workflows offered by ilastik, such as object classification, automatic tracking.

In the commercial image analysis software "Volocity", automated measurement protocols can be constructed by dragging, dropping and configuring a sequence of individual "tasks".

By combining the "Find Objects" task with a subsequent "Track" task, 3D objects can be identified and followed over time. The initial "Find Objects" segmentation can be refined, e.g. using "Separate Touching Objects"; and tracking results in the form of "Measurement Items" can be viewed in tabular form, as a graph, etc.