I am new on Fiji. I want to measure area and perimeter of bone and implant using weka segmentation. I trainned a sucessful clasifier, generated probability maps and created a 8 bits mask for each of the classifiers but… I’m not sure the way for the analysis. I know that it is a very simple question for all of you , however, if run analyze particles (as @etadobson pointed out in this thread) it generates a lot of ROIs wich don’t seem the areas that I want to measure. Please, could anyone help me?
But sadly, I still do not understand the analysis of the mineralized bone (=Brown areas), the masks and the ROIs are not ok. In the image below, this is the results of the ROIs, for example, the ROI number 20 (blue marked) is clearly not a brown area (mineralized bone) is a black area (implant). And it is including white areas (background). mineralized bone mask.tif (1.4 MB)
run("Scale...", "x=0.5 y=0.5 width=565 height=549 interpolation=Bilinear fill average create");
roiManager("Show All without labels");
Thanks for your suggestion, but let me make you a question. If I have a mask, in a binary mode, the threshold is not necessary isn’t it? So, I don’t understand how FIJI is doing the measures of this image:mineralized bone mask.tif (1.4 MB)
I am new on the image analysis field, perhaps my questions are so obvious for all of you.
If I am not mistaken, the particle analyzer works on the threshold set on the image. If the image is already binary, it is a good idea to make sure that the threshold has been set to the phase you want to analyse (and it appears red) otherwise you risk analysing the “other” phase.
Some other plugins might accept binary images and consider the object as the white or the black regions. The Particles8 plugin does not require a threshold but the user needs to specify whether to analyse the black or the white phase.
Yes you can use the histogram of the binary image to find out how many pixels are “the object”, but be aware that there are various definitions of “area” in the discrete domain. Pixel counts is one of such estimates.