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Open-source/user-friendly: We currently use Matlab for the image tiling and then load the image montages into CP, but we would like to provide an open-source and user-friendly imaging flow cytometry workflow also suitable for everyday biologists.Why we would love to have a montage module in CP: cif file already works nicely in CP, Lee Kamentsky has written an open-source reader for cif files available on BioFormats cif file contains hundreds of thousands of small images, each image has only ~7KB, and metadata such as channel number along with each image. A compensated image file (.cif file) is a proprietary file format of the imaging flow cytometers developed by Millipore.Parameters: size of the n-by-n grid, default: 32x32 grid yielding 32_32=1024 images per image montage (if you estimate that we reach the sweet spot in terms of computational speed with a larger/smaller grid then pls pick that value as default) Test files called testfile_.cif (and Matlab files from bioformats to access the cif file) are here: No need to save the image montages though (displaying would be nice), as we have the CP module save images. cif file and generates and saves the n-by-n image montages.

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The CP montage module could be based on our Matlab code cif_reader.m (file attached) which reads a. ** This isn't entirely accurate either, because these data type maximums are not correct. Nonzero values evaluate to True, so the image is first rescaled to its supported maximum intensity and then rescaled again according to the provided value. This boolean rescale is not passed into the call to read. It's first scaled by supported maximum intensity (option 2 or 3) and then scaled again by the user specified value.

We won't be dependent on python-bioformats to load images and rescale values (this is very important for 3D work).

It won't rescale all values to be teeny-tiny if metadata is missing and the data type is 32-bit integer (in this case, all values would be rescaled by dividing by 65535.

It's consistent and not dependent on image metadata.

Rescaling intensities in this way has the following benefits: That is, rescale intensities between 0 and 1 such that the actual minimum intensity value is mapped to 0 and the actual maximum intensity value is mapped to 1 (and all other intensities are adjusted accordingly): image = (image - image.min())/(image.max() - image.min()) Instead of defaulting to rescaling by supported maximum intensity (options 2 or 3), CellProfiler should default to rescaling intensities by actual maximum intensity.

The maximum value supported by the image data type**.

The maximum intensity supported by the image, as defined in the image metadata.

Let us know if you encounter a bug by submitting a GitHub issue.Ĭurrently, when loading an image, intensities are rescaled based on the following criteria:

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You can download a beta release for macOS and Windows from the CellProfiler website. If you’re an enthusiastic CellProfiler user, you should try the beta release of CellProfiler. Let us know if we’ve inadvertently broken your module by submitting a GitHub issue. You can download a nightly release for macOS and Windows from the CellProfiler website.

If you’re the maintainer of a third-party CellProfiler module, you should use the nightly release of CellProfiler.

Instructions for compiling CellProfiler on Linux, macOS and Windows are available from CellProfiler’s GitHub wiki. If you’re contributing or planning to contribute to CellProfiler, you should compile CellProfiler from source. You can download a stable release for macOS and Windows from the CellProfiler website. We recommend the stable release of CellProfiler. What version of CellProfiler should I use? More information can be found in the CellProfiler Wiki.

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CellProfiler is a free open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically.

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