mida-group/py_alpha_amd_release

Py-Alpha-AMD Registration Framework - Robust Image Registration - Public Release

Project: py_alpha_amd

Python/NumPy/SciPy implementation of a registration framework using the Alpha AMD similarity measure, which combines intensity and spatial information. If you use this, please cite: https://doi.org/10.1109/TIP.2019.2899947

The Alpha AMD similarity measure typically exhibits few local optima in the transformation parameter search space, compared to other commonly used measures, which makes the registration substantially more robust/less sensitive to the starting position, when used in a local search framework.

This framework consists of a collection of common transformation models which can be composed, combined and even extended with new transformations, driven by a gradient descent optimizer to find the transformation parameters which reduces the cost function.

Features

• The framework/measure supports images, represented by numpy arrays, with additional (anisotropic) voxel-sizes.
• 2D and 3D registration (ND for affine transformations)
• Completely python/numpy/scipy based codebase. No C/C++/... code, which should facilitate portability and understandability.

Example

Try out the provided sample script: python2 register_example.py ./test_images/reference_example.png ./test_images/floating_example.png

License

The registration framework is licensed under the permissive MIT license.

Author/Copyright

Framework was written by (and copyright reserved for) Johan Ofverstedt.

py_alpha_amd_release's Issues

RGB image

Hi, first of all thank you very much for your work, it has helped me a lot, I would like to use your method for aligning WSI. i actullay wanna to ask if it is possible to use raw RGB images as input instead of grayscale maps? If not, is it possible to apply the transformation obtained from the grayscale map to the RGB image?