Python for Medical Imaging¶
There are several reasons for choosing Python for Medical Imaging. First of all, the main biomedical imaging library out there, ITK, offers both exhaustive SWIG wrappers and a simplified interface called SimpleITK, which can be directly installed from PyPI. Other major libraries, such as VTK for data visualization and OpenCV for Computer Vision also provide excellent Python wrappers.
Then comes the Python eco-system itself, whose ressources for Machine Learning and Image Processing have been growing in the last years with the main libraries: scipy.ndimage, scikit-image and scikit-learn. There are also Python modules for input/output of medical images, such as pydicom and nibabel.
Finally, enough is never enough, there is cython to easily mix python and C/C++ code.
But there's more to Python than a set of existing libraries. It is simple to write, yet powerful. It has a wide community, much wider than the limited scope of scientific computing. It is Open Source, which is a key element for research. It offers several possibilities for optimizing code, for instance making an efficient use of numpy, using joblib for parallelization or cython for C++ optimization.
Then comes the Python eco-system itself, whose ressources for Machine Learning and Image Processing have been growing in the last years with the main libraries: scipy.ndimage, scikit-image and scikit-learn. There are also Python modules for input/output of medical images, such as pydicom and nibabel.
Finally, enough is never enough, there is cython to easily mix python and C/C++ code.
But there's more to Python than a set of existing libraries. It is simple to write, yet powerful. It has a wide community, much wider than the limited scope of scientific computing. It is Open Source, which is a key element for research. It offers several possibilities for optimizing code, for instance making an efficient use of numpy, using joblib for parallelization or cython for C++ optimization.
Why another Medical Imaging library in Python?¶
IRTK is a Medical Imaging library developped within the BioMedIA group at Imperial College London, initially built around Daniel Rueckert's non-rigid registration using free-form deformations. While ITK deals with ND-images, can read most image formats and has a wide range of applications ranging from cell microscopy to satellite images, IRTK is much more focused, its core component being image registration. It can read NIFTI files, and write NIFTI and PNG files, and all images in IRTK are 4D (X, Y, Z, T), with flat dimensions where necessary. The code is only templated other VoxelType.
The main reason I chose to write a Python interface for IRTK instead of using what already existed is that it is much easier to collaborate within the lab if we all use a same code base. Moreover, I wanted medical images to be as much pythonic as possible, namely a subclass of numpy.ndarray which would automatically update its spatial coordinates when cropped.
The main reason I chose to write a Python interface for IRTK instead of using what already existed is that it is much easier to collaborate within the lab if we all use a same code base. Moreover, I wanted medical images to be as much pythonic as possible, namely a subclass of numpy.ndarray which would automatically update its spatial coordinates when cropped.
