change the file name of the image stack.change the path to the image containing folder.More frames for averaging results in less noise. In other image sequences, this value could be larger depending on the result of running averaging. We use the sample image eb1_8bit.tif in the following workflow. This preprocessing averages several framed before and after the current frame, and will reduce noise in background to have a smooth image sequence, which is better for detecting optical flows / movements. For this, install Running Z Projector written by Nico Stuurman. PIV plugin: as the PIV plugin depends on OpenCV library, please read the installation section of the PIV plugin website carefully and follow the instruction.įor most type of time-lapse sequences, preprocessing the sequence by running average (also called “box averaging or moving averaging”) is recommended.These results are saved in the folder declared in #3 above. The full path to which should be directly written in the script (see Usages).ĭisplacement vector fields as sequence of images. For more details, see the plugin site.Īn image stack. The script presented here is an add-on for Qing Zong Tseng’s PIV plugin, to batch process multiple images and outputs a sequence of vector fields. I implemeted the gradient method as a Igor Procedure and is available for downlaod: Vec2D package. One ImageJ plugin is available that implements the gradient method (generally called optical flow): FlowJ (or also see for a link to github repo). This PIV plugin takes two 2D images and estimate the displacement vector field between two time points. Two ImageJ plugins use the cross-correlation methods: one by Jean-Yves Tinevez ( PIV analyzer), and another one by Qing Zong Tseng ( PIV plugin). Two different algorithms are known for the computation of vector field: the gradient method and the cross-correlation methods. The results are typically presented as a vector field, where movement vectors are plotted at regular intervals in the image. Particle Image Velocimetry (PIV) is a way of quantifying speed and direction of movement occurring within video sequences. The plot FTTC function can be used to plot both the force field as a color coded vector plot or a force magnitude stress map.IJscript PIV Speed Measurement Vector Field X coordinate of the data (pixel) | y coordinate of the data (pixel) | x component of the traction force (pascal) | y component of the traction force (pascal) | force magnitude The output force field is a text file with each column separated by space. If you have problem finding the automatically saved force field file, try to put your PIV file in a folder that ImageJ could access without problem, ex: Desktop\ or ImageJ\ ) (In some cases, ImageJ under Windows system might have access problem to other folders. The reconstructed force field will be saved automatically at the same folder as the PIV file. Otherwise, if you can't get the OpenCV template matching run correctly, you can choose to do PIV by conventional cross-correlation by setting the "search window size" equal to the "PIV window size".Īt the end of PIV run, you will be asked to save it or do further filtering operation.Īfter obtaining the PIV data file, you can use the FTTC plugin to reconstruct the force field. But you need to have the OpenCV template matching plugin running correctly. The best result is obtained by using the normalized correlation coefficient algorithm where the interrogation window is searched against a larger search window. The displacement field is calculated by the iterative PIV plugin. You can do a pre-alignment to correct the experimental shift by using the Align slices in stack plugin, or any other slice registration plugin. Two fluorescent bead images should be combined as an ImageJ stack, with the null force image( without cell) as the 1st slice and stressed image (with cell) as 2nd slice.
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