PIV TOOL IMAGEJ HOW TO

This mask should contain all possible localizations for that stain/dye, or it should be a mask of localization for your null hypothesis ( i.e if you hypothesize a protein is localized to the mitochondria, you would want your mask to encompass the entire cell).

To get the best possible results, you will want to create and save a segmented mask for one of your images.

Randomization mask: An example of an appropriate mask for analyzing cross-correlation of cytoplasmic proteins(right), generated from an actin stain (left). Prepare a mask for the pixel randomization

Measure and subtract the mean background for both images (Process > Math > Subtract).

Convert images to 32-bit (Image > Type > 32-bit).

Deconvolve the images (Optional but recommended).

Prepare a mask of the region to be analyzed.

Verify accurate scaling (Image > Properties).

PIV TOOL IMAGEJ HOW TO

How to use Colocalization by Cross Correlation (CCC): Brief overview Installing the Plugin:Īvailable on the list of ImageJ updates sites. It currently works on 2D/3D single-channel images, supports time-series analysis, and requires a mask of all possible localizations for the signal in one of the images. It does this by performing a cross-correlation function (CCF) between the two images, operating in a similar manner to Van Steensel’s CCF except this plugin performs the CCF in all directions and provides additional information, such as the standard deviation and statistical measures. This plugin attempts to determine: the average distance between non-randomly spatially associated particles, the standard deviation of that distance (which should also reflect the width of the PSF in the image for diffraction limited images), and two statistical measures of the association.

Piv tool imagej