Lately i am trying to coniugate Fiji and CellProfiler to create a workflow to quantify fluorescence intensity in a big batch of images from different experiments.
So, to be clear, i work on zebrafish and i want to quantify the fluorescent signal in different areas of the zebrafish itself a different timepoints. Beside the different timepoints, i have other experimental parameters that i change on purpose.
Each different larvae is imaged alone on an EVOS FL auto in 3 channels: Transmitted Light, GFP and mCherry. So, i manually place each fish and change channel to acquire the image. Every fish will have 3 images with the 3 channels separated.
To try to give a logic to the process, i kept the light and gain parameters respectively to maximum and minimum, changing only the exposure time. To change the exposure times i sent the signal to saturation, where red pixels appears, and slowly decrease the exposure until the saturated pixels disappear.
Already on this aspect, i would like a feedback from you, to know if i use the right approach in acquiring the images.
To analyse the images i am trying to decide if it’s better to measure the intensity or the area of positive signal using threshold.
For now, i use the second method in CellProfiler, where i use a semi automatic method to select different area of the fish and measure the percentage of positive signal in said area (one of the aims of the experiment). So i have positive area (from thresholding) over total image area (that is always the same)
The problem, with both methods, is the heterogeneity of the images. That’s why i was thinking about creating a little script in fiji to normalise the images, but i am a little lost on which approach to follow.
Do you think the thresholding method is a good way to proceed and how you would normalise the images to make it more reliable and reproducible?
Thank you for the long read and the help!