Measuring the distance between neighbor irregular particle ( lamellar)

Hey there.

I’m pretty new to image analysis so this might seem a minor problem, but I have a microstructure for aluminum alloy and I am interested to know the distance between each lamellar particle and its neighbors’ particle ( black objects), I would like to measure the distances from the edges not from the centroid. please find the photo and thank u in advance:slightly_smiling_face::slightly_smiling_face:

MD-Experiment-0001_p015.tif (1.4 MB)

Hi @kassabalomari1992,

Here a simple approach of how to do :

  1. Get the individual objects with analyse particles and using count mask
  2. Use the 3D distances plugin from 3D ImageJ suite, select border and closest 2

Here some results, first the individual labeled objects :

MD-Experiment-0001_p015-1

Then the results of border-border distances :

Screenshot_20190917_141735

Another approach would be to measure the space between objects, for instance using the EDM distance map directly from the original image :

MD-Experiment-0001_p015-1-EDM

Hope this helps,

Best,

Thomas

1 Like

Thank you for your help, it is great and useful ideas. I am trying to use the 3D ImageJ suite but it always shows one result in the table ( please see the photo below). is there a tutorial or explanation material about this plugin. thank you so much again

Regards,
Kassab Al-Omari

Hi @kassabalomari1992,

Before any analysis you need to detect the individual objects, this is called labelling. Since your image is already binary, with objects in black and background in white it is easier to label the different objects :

  1. Invert the image (edit/invert)
  2. Label the image with analyse particles (show Count mask, everything unchecked). You can also adjust the parameter for minimum size to remove smaller objects.
  3. Perform the distance analysis

In a macro it should look like this :

run(“Invert”);
run(“Analyze Particles…”, “size=20-Infinity show=[Count Masks]”);
run(“3-3-2 RGB”);
run(“Enhance Contrast”, “saturated=0.35”);
run(“3D Distances (beta)”, “image_a=Count image_b=Count compute=Closest_2 closest=Border(slow) border”);

Best,

Thomas

Thank you so much, it was good solution for me .
Again I appreciate your help.

Best wishes,
Kassab Al-Omari