There are two tracking algorithms:
Track Objects – tracks binary objects by finding their intersections over time. It is intended for large non-rigid objects (objects whose center doesn't represent position) moving slowly compared to the FPS (object must overlap frame-to-frame).
Track Particles – tracks coordinates (from table) over time. It is intended for small rigid particles.
In this example the number of Focal Adhesions (FA) per cell in time is tracked.
Getting the number of FA per frame was described earlier (see Aggregating children).
First it is necessary to properly detect the Cells, FAs and measure the features of interest.
Figure 822. Analysis definition.
Figure 823. Original image (left) and thresholded image (right) showing Cells (purple) and FAs (yellow).
“ObjectId” can change over time so it is necessary to perform tracking in order to relate the objects in time (
Display Tracking is turned on).
Figure 824. Tracking results.
All the results are combined using the Append Columns node (since all records contain the same objects).
Figure 825. Appending results.
And then aggregated together.
Figure 826. Aggregating results.
Figure 827. Tracking result.
In this example, algae are tracked and their speed is determined. Because the objects move fast compared to the sampling frequency, TrackParticles node has to be used.
Figure 828. Analysis definition.
Figure 829. Tracked algae cells with their object ID's. Timelapse frame #1.
Figure 830. Tracked algae cells with their object ID's. Timelapse frame #44.
As TrackParticles takes table with space coordinates and time as input we have to measure these features. We can use the convenience composed node TimeAndCenter.
Then we add TrackParticles and fill the parameters in the GUI.
Figure 831. Track Particles node settings.
After that we can filter out segments results with minimal segment count of 1 using the Accumulate Tracks node.
Figure 832. Accumulate Tracks node settings.
Using the Export Tracks node we can export all tracks to the current document. Tracks can be viewed directly in the image once Display Tracking is turned on.
Figure 833. Track visualization.
Figure 834. Export Tracks results in GA3.
The same tracking results can be opened inside the 
Tracking panel.
Figure 835. Track results in the Tracking panel.
To measure track features, we can use the Motion Features node.
Figure 836. Motion Features node settings.
To calculate the relative motion of a cell with origin in its first position we can use the Position Integrate node.
Figure 837. Position Integrate node settings.
Then we can visualize the relative movement using a Scatterplot.
Figure 838. Scatterplot data settings.
Figure 839. Scatterplot X Axis settings.
The scatterplot is shown as a new tab in the analysis results.
Figure 840. Resulting graph showing the particle relative motion.
This example shows how to track live cells using a drawn ROI. The ROI follows movement of the selected cell and the stage is moved accordingly to capture images during a longer period of time.
Start by thresholding the cells of interest (Bright Spots), then define a rectangular ROI around the cell of interest (Draw Rectangular ROI).
Figure 841. Original image.
Figure 842. Max IP image showing the cell movement.
Figure 843. Analysis definition.
Figure 844. Detected cells (purple) and rectangle ROI (green).
Cell present in this rectangle is further tracked (Track Objects) and Time & Centroid is calculated. Both records are appended and Centroid X and Y Diff Position is calculated for the use in JOBs.
Figure 845. Diff Position node.
Figure 846. Tracking results showing the Appended Columns and Diff Position.
Inside JOBs a new time lapse is created capturing images ever 2 minutes exactly in the center of the centroid taken from the previously defined general analysis. The stage is automatically moved to the center using the “StgMove” macro function.
Figure 847. JOBs definition.