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        "author": "Michael Hogg",
        "author_email": "michael.christopher.hogg@gmail.com",
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            "Development Status :: 4 - Beta",
            "Environment :: Other Environment",
            "Intended Audience :: Healthcare Industry",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering :: Medical Science Apps.",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "==========\r\nBMDanalyse\r\n==========\r\n\r\n.. image:: https://img.shields.io/pypi/v/bmdanalyse.svg\r\n   :target: https://pypi.python.org/pypi/bmdanalyse/\r\n   :alt: Latest PyPI version\r\n\r\n.. image:: https://img.shields.io/pypi/dm/bmdanalyse.svg\r\n   :target: https://pypi.python.org/pypi/bmdanalyse/\r\n   :alt: Number of PyPI downloads\r\n\r\nCopyright 2016, Michael Hogg (michael.christopher.hogg@gmail.com)\r\n\r\nMIT license - See LICENSE.txt for details on usage and distribution\r\n\r\n-----\r\nAbout\r\n-----\r\n\r\nA graphical tool used for the regional analysis of a time series of 2D medical images. This was created for the analysis of virtual x-rays, created by companion tools `bonemapy`_ and `pyvXRAY`_ from the results of a computer simulation. Intended to be used to evaluate the bone gain / loss in a number of regions of interest (ROIs) over time, typically due to bone remodelling as a result of stress shielding around an orthopaedic implant.\r\n\r\nWritten in pure Python using PyQt/PySide, pyqtgraph, numpy, matplotlib and pillow. Should work on any platform, but has only been tested on Windows.\r\n\r\n.. _bonemapy: https://github.com/mhogg/bonemapy\r\n.. _pyvxray: https://github.com/mhogg/pyvxray\r\n\r\n------------\r\nRequirements\r\n------------\r\n\r\n* Python 2.7\r\n* PyQt >= 4.11\r\n* pyqtgraph >= 0.9.10\r\n* numpy >= 1.9\r\n* matplotlib >= 1.4\r\n* pillow >= 3.0\r\n\r\n**NOTE:** All these requirements are available within the Anaconda Python distribution\r\n\r\n--------------------\r\nInstructions for use\r\n--------------------\r\n\r\n- Load a time series of 2D medical images (in image format such as bmp, png etc). All images should be grayscale and of the same size.\r\n- Use the up / down arrows in the Image toolbox to place the images in chronological order. A time series of virtual X-rays is provided in the sampleMedicalImages directory. \r\n- Create some Regions of Interest (ROIs) and run the ROI analysis tool from the Analysis toolbox. A plot will be generated showing the change in the average grayscale value with each ROI over time.\r\n- Run the Image analysis tool from the Analysis toolbox. A contour will be displayed, showing regions of bone loss in blue and bone gain in red.",
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