You may need to invert the images first with ImageMath (as below) if the objects you are trying to detect are darker than the background. The filter can be used as a nuclei / spots detector in fluorescence images or (to some extent) can detect cells in brightfield images. So choose a high value and go down from there (test mode is your friend). Sometimes it’s advantageous to clip the high intensity values. The high value depends on what you’re trying to detect. So thresholding at 0 or maybe just above 0 is important. Thresholding is important to use with this filter, because each scale may have positive and negative values. ![]() So scale 1 would isolate features of 1-2 pixels in diameter, scale 2 2-4, scale 3 4-8, etc… So if you wanted to detect nuclei which are roughly 20 pixels in diameter and eliminate speckle noise (scale 1) as well as the image background, you could try to select scale 4 or scale 5 or scales 3-5. Roughly (in my mind), scales correspond to a feature size of 2^(scale_index-1) to 2^(scale_index). The filter uses combined “scales” to define a range of object / feature sizes to detect in the image. The “A trous” filter works pretty well for detecting roundish objects of similar sizes in an image, even in the presence of speckled noise / high background. I do have a 2-D / 3-D plugin for CellProfiler 3.0 but I need to clean it up a bit before I release it. I finally managed to release my optimised 2-D / 3-D libatrous library, as well as a simple CellProfiler plugin (2-D), which I often use in CellProfiler 2.4 pipelines for cleaning images before “IdentifyPrimaryObjects”.
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