[Insight-developers] Registration classes

[Daniel J. Blezek, Ph.D.]

Luis,

  I've been going through the Insight code looking at header files.  These
do not compile, due to a missing itkKalmanFilter.h file:

itkRegistrator3D2D.h
itkRegistrator3D2DBatch.h
itkRegistrator3D2DRecursive.h
itkKalmanLinearEstimator.h

If these are outdated due to the new hierarchy, could you remove them from
the repository?

Thanks,
-dan


On Fri, 15 Dec 2000, Luis Ibanez wrote:

> Hi,
> 
> We checked in some classes that could support a full
> hierarchy for registration. They are described in 
> "RegisrationArchitecture.doc" in the repository's 
> Document directory.
> 
> The basic class is called:
> 
>       RegistrationMethod
> 
> It is intended to support registration of any kind
> of objects, no only images.
> 
> It groups the basic elements of a registration
> problem:
> 
>  - a RegistrationMetric to evaluate the match between 
>    the objects to be registered (target and reference)
> 
>  - a RegistrationTransformation that defines how the 
>    reference object will be mapped on top of the target
> 
> -  a RegistrationMapper that having access to the 
>    transformation will pass information back and forth 
>    between the reference and target space. Eventually 
>    doing some interpolation on the way.
>    
> -  an RegistrationOptimizer that search for the optimum
>    transformation.
>  
> 
> The Optimizer is expected to be only a light wrapper of an
> already existing optimization class. The first candidates
> to wrap are the vxl algorithms:
> 
>    vnl_nonlinear_minimizer (the generic api)
>    vnl_conjugate_gradient
>    vnl_levenberg_marquardt
>    vnl_amoeba
>    
> and then, some genetic (or evolutionary) algorithms.
> 
> 
> The optimizer's search space is the one defined by the
> transformation parameters. That allows to separate the 
> implementation of the transformation from its definition.
> For example a 2D rigid transformation can be implemented
> as a 3x3 matrix in homogeneous coordinates, but it is 
> defined by just three numbers (parameters) that represent:
> ( rotation, translation x, translation y).
> 
> 
> The Metric will evaluate how much the transformed reference 
> match the target objet. Possible Metrics to implement are:
> 
>    Intensity Correlation (for images)
>    Intensity based Mutual Information
>    Intensity Squared differences (for images)
>    Sum of squared distances (for point sets)
>    
> 
> On the same spirit, the RegistrationTransformation will be
> a light wrap for geometric transformations, like the already
> existing itkAffineTransform. It just add some mechanism to
> standarize the passing of parameters to the optimization
> method. Some transformations to consider are:
> 
>    Rigid 
>    Similarity 
>    Affine
>    Thin Plate Spline (defined by landmarks)
>    Flow 
> 
> 
> The basic objects can be used to derive from them, or can 
> just be used as examples of what elements the type should
> have.
> 
> 
> Some examples of how this code will look like when used
> in a real registration problem are in the Testing directory,
> they compile on Linux and VC++. (There are actualy not 
> doing any thing different than checking that all the types
> are coherent).  They are:
> 
>       itkRegistrationImageToImageAffine.cxx
>       itkRegistrationModelToImage.cxx
>       itkRegistrationPoints3D2DAffine.cxx
>       itkRegistrationProcrustes.cxx
> 
> 
> -----
> 
> 
> Thanks
> 
> 
> Luis
> 
> 
> -- 
> -----------------------------------------------------------
> Luis Ibanez
> Research Assistant Professor - Division of NeuroSurgery
> University of North Carolina at Chapel Hill
> Chapel Hill, NC, 27599   http://www.cs.un.edu/~ibanez
> ------------------------------------------------------------
> 
> 
> 
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> 

--
Daniel Blezek, Ph.D.
blezek@crd.ge.com
Visual Information Program
Electronic Systems Lab
GE Corporate Research & Development