medical image analysis pymia
Libraries and command line tools for medical image processing.
Brought to you by:
gerddie,
gertwollny
File | Date | Author | Commit |
---|---|---|---|
src | 2014-09-01 | Gert Wollny | [072ff6] update copyright year |
.gitignore | 2013-10-16 | Gert Wollny | [d7a954] update ignore files |
AUTHORS | 2012-03-26 | Gert Wollny | [8c54f1] initial checkin |
COPYING | 2012-05-18 | Gert Wollny | [9d3970] add copyright info |
ChangeLog | 2016-06-03 | Gert Wollny | [f18b1a] update Changelog |
MANIFEST.in | 2013-10-23 | Gert Wollny | [0660c0] correctly add header files to source distribution |
README | 2013-12-04 | Gert Wollny | [882630] remove the python tools as they are not python3 |
setup.py | 2016-07-27 | Gert Wollny | [2b273c] move to mia-2.4 |
This is the python interface for the MIA library. It uses numpy to represent images, and currently, filters and image registration is supported. Compiling the interface: The build system utilizes python distutils. To compile the interface the compiler must support the -std=c++11 compiler flag. Building the interface then build down to running python setup.py build Note, that you wil need mia >= 2.0.10 to be installed. Using the python interface: Given that MyImage is a 2D or 3D numpy array of scalar values, one may run a filter chain like import mia MyFilteredImage = mia.filter(MyImage, ["filter1:param1=a", "filter2:param1=b,param2=c", ...]) For the availabe filters see the user reference of MIA. Similarly, the image registration can be run with images Moving and Reference, using a spline based transformation with a coefficient rate of 5 pixels, 2 multi-resolution levels and an nlopt based optimizer: import mia Registered = mia.register_images(src=Moving, ref=Reference, transform="spline:rate=5", cost=["image:cost=ssd", "divcurl:weight=10.0"], mglevels=2, optimizer="nlopt:opt=ld-var1,xtola=0.001,ftolr=0.001,maxiter=300") The result image is stored in Registered. Currently, the transformation is lost.