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        "author": "Gautam Munglani",
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        "description": "# FRET - Image Background-subtracted Ratiometric Analysis (FRET - IBRA)\n\nFRET - IBRA is a toolkit to process fluorescence resonance energy transfer (FRET) intensity data to produce ratiometric images for further analysis. This toolkit contains modules for the background subtraction (using an algorithm based on tiled DBSCAN clustering), image registration, overlap correction, and bleach correction of the donor and acceptor channels. It accepts multi-image TIFF stacks as input and outputs both multi-image TIFF and HDF5 stacks for possible further analyses, along with frame-by-frame metrics to estimate quality. The background subtraction algorithm works best on images with a small number of cells visible in the frame.\n\n\n## Installation\n\nUse the package manager [pip](https://pip.pypa.io/en/stable/).\n\n```bash\npip install fret-ibra\n```\nAdditional requirements: ffmpeg\n\n## Usage\n\n```bash\nUsage: ibra -c <config file> [Options]\nOptions: -t   Output TIFF stack\n         -v   Print progress output (verbose)\n         -s   Save as HDF5 file\n         -a   Save background subtraction animation (only background module)\n         -e   Use all output options\n         -h   Print usage\n```\n\n## Examples\n\n### Acceptor channel input image\n![YFP](/examples/images/YFP_input.png)\n\n### Donor channel input image\n![CFP](/examples/images/CFP_input.png)\n\n### Ratiometric output image (8-bit)\nProcessing includes:\n* Background subtraction for both channels\n* Image registration\n* Overlap correction\n* Bleach correction\n\n![Ratio](/examples/images/Ratio_output.png)\n\nA detailed explanation of the toolkit can be found here: [Tutorial](/examples/Tutorial.md)\n\n\n",
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        "keywords": "opencv,background subtraction,DBSCAN clustering,FRET imaging,ratiometric",
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        "name": "fret-ibra",
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        "summary": "FRET-IBRA is used to process fluorescence resonance energy transfer (FRET) intensity data to produce ratiometric images for further analysis",
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