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Tool to analyse regional changes in a time series of 2D medical images.

Project description

Latest PyPI version Number of PyPI downloads

Copyright 2016, Michael Hogg (

MIT license - See LICENSE.txt for details on usage and distribution


A 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.

Written in pure Python using PyQt/PySide, pyqtgraph, numpy, matplotlib and pillow. Should work on any platform, but has only been tested on Windows.


  • Python 2.7

  • PyQt >= 4.11

  • pyqtgraph >= 0.9.10

  • numpy >= 1.9

  • matplotlib >= 1.4

  • pillow >= 3.0

NOTE: All these requirements are available within the Anaconda Python distribution

Instructions for use

  • 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.

  • 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.

  • 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.

  • 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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