SimpleITK 0.9.0 has been released!

SimpleITK 0.9.0 has been released!

Get it now!

Here is a quick overview of the ITKv4's registration in SimpleITK via IPython/Jupiter notebook.


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.


Questions or comments are always welcome!