Hi, I was trying to do cell counting analysis on some of my cultured rat astrocytes whose boundaries are quite indistinct as the cells are flat and confluent, by watershed after having done some adjustment to the images. The main problem I think I have here is that the colour intensity of the “background” is almost the same as that in the “cell body” on the images. How should I approach it?
would you mind sharing an example image? We could then suggest a strategy most effectively
You can use machine learning to segment your image. With Trainable Weka Segmentation (in Fiji: Plugins > Segmentation > Trainable Weka Segmentation
Please don’t use .jpeg files
In the settings, I had added the Variance and Structure features, increased the maximum sigma to 32, and checked Homogenize classes , see also the screenshot:
Hope that helps.
Thank you so much for your reply! I seem to be able to at least get a relatively accurate representation of the area that my rat astrocytes were occupying using Trainable Weka Segmentation.
Is there a way to measure the black area on this image? (it is a tiff image but I only attached a screenshot here.)
Also, is there a way to do the segmentation action on a large scale, i.e. with multiple images at one time.
I’m a bit confuse if you are able to measure the white area, the black area = total area - white area.
This is some link fr your macro