![]() Since this method relies on first clipping all the true signal out of the image based on a percent of the image, it does NOT perform well when some images in a dataset have 5% of pixels with true signal and other images have 25% of pixels with true signal. The threshold is then calculated by adding the average to N * the variance. It then calculates the central value for the background intensities and calculates the variance within that population. How does the Robust Background method work, then? This algorithm first trims the brightest and dimmest pixel intensities to remove outliers and the true signal so that all the pixels that remain represent only the background intensity levels. If you’re working with images that look like the image in (B), then I’d recommend trying out the Robust Background method instead! ![]() Since this image has only one visible peak at the low intensity values (reflecting the fact that most of the pixels in the image are background), a peak fitting approach won’t work well. While there ARE a few pixels with brighter intensity there aren’t enough to form a visible peak in the histogram. The histogram for this image shows only a low intensity peak. In contrast, panel B shows an image that is mostly background with a few bright bright spots that cover very little of the image (most of the image is black). The histogram for the image in (A) shows a peak of low intensity values and a peak of high intensity values. Panel A shows an image of a nuclear stain where the nuclei occupy ~ 30% of the image. In order to understand these differences, we’ll start by visualizing histograms of intensity values for two different images. The Robust Background method, on the other hand, works well when the image is predominantly background (one peak only at low intensity). Methods such as Otsu and Minimum Cross Entropy attempt to fit peaks to separate the background (peak at low intensity) from objects of interest (peak at high intensity). The automatic thresholding methods in CellProfiler calculate a pixel intensity threshold where any value greater than that threshold is considered foreground and any value below that threshold is considered background. Visualizing the distribution of pixel intensities using a histogram can be a very useful way to understand and select an appropriate thresholding method for a dataset. However, since it contains the largest number of tunable parameters of any thresholding algorithm in CellProfiler, it can be confusing to configure and understand how this method works. Pkg_resources.ContextualVersionConflict: (matplotlib 1.5.1 (/usr/lib/python2.7/dist-packages), Requirement.parse('matplotlib>=2.0.The Robust Background algorithm is a powerful algorithm for automatically setting thresholds to segment objects of interest when your image contains mostly background. Raise VersionConflict(dist, req).with_context(dependent_req) Return cls._build_from_requirements(_requires_)įile "/usr/local/lib/python2.7/dist-packages/pkg_resources/_init_.py", line 593, in _build_from_requirementsįile "/usr/local/lib/python2.7/dist-packages/pkg_resources/_init_.py", line 786, in resolve Cell profiler seems to be running using python2.7, not python3.6 which is also set up on this machine, so this could be the source of the problem?Įrror message in full: Traceback (most recent call last):įile "/usr/local/bin/cellprofiler", line 6, in įrom pkg_resources import load_entry_pointįile "/usr/local/lib/python2.7/dist-packages/pkg_resources/_init_.py", line 3112, in "/usr/local/lib/python2.7/dist-packages/pkg_resources/_init_.py", line 3096, in _call_asideįile "/usr/local/lib/python2.7/dist-packages/pkg_resources/_init_.py", line 3125, in _initialize_master_working_setįile "/usr/local/lib/python2.7/dist-packages/pkg_resources/_init_.py", line 580, in _build_master However when I attempt to run cellprofiler from terminal, i encounter the following error code, which seems to be telling me there is a version conflict of matplotlib. Im trying to run the bioimaging analysis package 'cell profiler' on Ubuntu 16.04, following the source installation instructions provided by the developers:
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