SimpleITK 0.9.0 has been released!
SimpleITK 0.9.0 has been released!
Here is a quick overview of the ITKv4's registration in SimpleITK via IPython/Jupiter notebook.
l
This release features the ImageRegistrationMethod which brings a SimpleITK style interface to the modular ITKv4 registration framework. This adds support for a variety of transforms including rigid, affine, b-spline, and deformation fields. The metrics supported include correlation, means squares, ANTS neighborhood correlation, and mutual information. A variety of optimizers are available along with scales estimators for the optimized transformation parameters and built in multi-scale registration support.
Additionally, a number of registration filters have been added:
- DemonsRegistrationFilter
- DiffeomorphicDemonsRegistrationFilter
- FastSymmetricForcesDemonsRegistrationFilter
- LevelSetMotionRegistrationFilter
- SymmetricForcesDemonsRegistrationFilter.
Several examples can be found in the examples directory to help you get started. These examples include Affine registration, BSpline, Demons and Displacement fields.
The following filters were also added:
- AdditiveGaussianNoiseImageFilter
- AggregateLabelMapFilter
- BinaryImageToLabelMapFilter
- ChangeLabelLabelMapFilter
- CollidingFrontsImageFilter
- DisplacementFieldJacobianDeterminantFilter
- FastMarchingBaseImageFilter
- FastMarchingUpwindGradientImageFilter
- InverseDisplacementFieldImageFilter
- InvertDisplacementFieldImageFilter
- LabelImageToLabelMapFilter
- LabelShapeStatisticsImageFilter
- LabelStatisticsImageFilter
- LabelUniqueLabelMapFilter
- MergeLabelMapFilter
- RelabelLabelMapFilter
- SaltAndPepperNoiseImageFilter
- ShotNoiseImageFilter
- SpeckleNoiseImageFilter
- TransformToDisplacementFieldFilter
There is more Information on how to get started and download the binaries and in the release Doxygen documentation along with additional release notes.
Enjoy!