Once you have a good enough classifier, you can save it ( Save classifier) and apply it to many more images in batch ( Apply classifier). You can also control which image features are used for classification in the Settings dialog, which might help the classification. Unfortunately, on my first attempt the cell borders also trained as class 2, but with a bit more tuning perhaps that problem could be eliminated. And since the median lines are directional you could even compute angles for cell orientation, if that is useful for your analysis. Once you have the centers the problem of overlapping cells largely disappears. My idea was to isolate those median lines, since from them it is easier to find cell centers, and your cells are very regular in shape. I divided the image into four classes: background, the dark area in the center ("blob"), the cells themselves ("class 1") and the median line running through each cell ("class 2"). I played a bit with Trainable Weka Segmentation and got some potentially promising, but still-not-yet-good-enough, results: ![]() In your case, I would suggest giving the former a try. So far, your attempts fall more into the latter category. The Fiji wiki page on Segmentation discusses two primary ways of approaching image segmentation: the Trainable Weka Segmentation plugin, and a more flexible macro-based workflow. Overlapping objects can be a tough problem. Pleas help (either with the first or second code!) Thanks! :) Run("Auto Threshold", "method=Otsu white") ImageCalculator("Subtract create", a,"_seed") Run("GreyscaleReconstruct ", "mask="+a+" seed=_seed create") I also tried using GreyscaleReconstruct but I was not that successful either. I am using the macros on a stack of images. Run("Analyze Particles.", "size=200-2000 circularity=0.50-1.00 show= display exclude clear summarize add in_situ") Run("Auto Threshold", "method=Yen white stack") Run("Subtract Background.", "rolling=5 light sliding stack") However, I have a hard time removing the overlap between cells and for the program to distinguish between the clumps. I have been trying to make a macros for counting the cells in the image.
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