I want to measure cells and their nucleus automaticly with imagej, but I haven't any ideas how to do it. I know about function "Find Edges", but I don't know what to do next
I used your question to search google, and several YouTube tutorials were returned as the top hits. You may want to start by looking at them. Also, do you want to measure area, signal intensity, length, or something else? I noticed from your image, that there is not good definition of the edges between cells. You may need to either use a different stain so that cell edges are defined better, or you may need to alter your image in an image manipulation program to make the edges stand out better. I also did a google search using "imagej find edges measure cells" and found a few useful blog posts and wiki pages like:
please elaborate question, whether you want to quantify area, number, intensity of a particular signal(e.g., DNA or a protein).
quantifying nuclei (assuming ploidy):
if you are trying to quantify any signal from microscopy images, you have to take care of several parameters.
step 1: find suitable protocol for fixing your sample and staining with fluorescent DNA binding dyes like DAPI or PI.
step 2: proper image acquisition
1. flat field correction 2. control slides 3. presence of IR filter to camera 4. finding linearity of your system. 5. stability of your light source of microscope throughout experiment. and some other controls to make sure your microscope is working well for quantification.
make sure that you have't saturated image while grabbing and using flat field correction. if you have taken image with color camera and now converting it to black n white doest give good results. and images that are not corrected for flat field can change intensity of similar intensities upto 40%.
for this you just need to stain your nucleus which can be imaged with different channel from rest of cell. their are several videos in youtube how count number, area etc.. with image J.
quantifying cell:
as per i understand, cells in your image are too crowded for identifying individual cells by most of image processing algorithms. try to decrease density of them if possible. if not, you have to find a suitable mechine learning program to do that.
ilastic is one of the popular machine learning software.