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.

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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!

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