[Insight-users] 3d Deformable Registration problem based on DeformableRegistration8

Tue May 12 12:02:24 EDT 2009

Hi Albert,

It turned out in practice that the LBFGSBOptimizer is not as superior
as it may have been assumed.

Lately we have simply used the RegularStepGradientDescentOptimizer
with reasonable results.

See for example:

     Insight/Examples/Registration/DeformableRegistration15.cxx

In general,
any claims saying than some component is

                              "The Best"

should be taken with a lot of suspicion.

Such claims are good only for publishing papers in traditional
Journals and Conferences that do not exercise reproducibility.
You can not rely on such sources when you are working on
serious applications.

Don't hesitate to experiment with other components of the
registration framework.

In many cases, the challenge is not so much about what components
to use, but about how to fine-tune their parameters for the problem
at hand.

Please let us know what you find,

     Thanks

          Luis

-------------------------
On Tue, May 12, 2009 at 11:21 AM, Albert Gubern <bertkrek at gmail.com> wrote:
> Hi,
>
> I've modified the example DeformableRegistration8.cxx in order to do a 3D
> DeformableRegistration. This example uses MattesMutualInformationMetric,
> BSplineDeformableTransform and LBFGSBOptimizer.
>
> I tested the code with a brain phantom generated with Matlab. The original
> volume is deformed using Matlab b-spline deformation based on this routine
> http://www.mathworks.com/matlabcentral/fileexchange/20057, and I want to use
> the deformable registration of ITK to recover the deformation. The next link
> shows two checkerboard of one slice, before and after the succesful
> registration:
> http://img140.imageshack.us/img140/1831/phantomdeformableregist.png. The
> original volume is set as the fixed image and the deformed phantom as the
> moving.
> - Mostra el text citat -
>
> The output of the execution is:
>
> Starting Registration
> 0   -0.579387   0 0
> 0   -0.581355   0 0
> 0   -0.584345   0 0
> 0   -0.584345   6.33911e-05 0
> 1   -0.603649   6.33911e-05 0
> 1   -0.603649   6.41689e-05 0
> 2   -0.624884   6.41689e-05 0
> 2   -0.624884   7.76054e-05 0
> 3   -0.638071   7.76054e-05 0
> 3   -0.638071   6.32524e-05 0
> 4   -0.655438   6.32524e-05 0
> 4   -0.655438   7.51525e-05 0
> 5   -0.677776   7.51525e-05 0
> 5   -0.677776   6.37494e-05 0
> 6   -0.696669   6.37494e-05 0
> 6   -0.696669   5.09743e-05 0
> 7   -0.713613   5.09743e-05 0
> 7   -0.713613   5.48518e-05 0
> ... (60 iterations more)
> 67   -0.854863   4.01611e-05 0
> 67   -0.854863   4.01611e-05 0
> 67   -0.854863   4.01611e-05 0
> 67   -0.854863   4.01611e-05 0
>          Probe Tag    Starts    Stops             Time (s)
>        Registration           1            1           3289.35
>
> Next step was to test the same code with prostate MRI images but the
> registration doesn't work. The original volume is a central region of MRI to
> focus the registration on the prostate
> (http://img145.imageshack.us/img145/200/mrireal.png), and the same
> deformation, that was applied to the phantom test, is used to deform the
> original prostate. The goal of the registration is the same: recovering the
> deformation. The next link is a checkerboard of one slice of the volume to
> show that fixed and moving images look different enough
> (http://img26.imageshack.us/img26/8905/im011.png).
>
> In this test the program the registration stops too early doing only few
> iterations and the InfinityNormOfProjectedGradient is always zero:
>
> Starting Registration
> 0   -0.742878   0 0
> 0   -0.722791   0 0
> 0   -0.721167   0 0
> 0   -0.721134   0 0
> 0   -0.733701   0 0
> 0   -0.733701   0 0
> 0   -0.742878   0 0
> 0   -0.742878   0.000122748 0
>          Probe Tag    Starts    Stops             Time (s)
>        Registration           1            1           97.3459
>
> I have tested the registration using different values of bins
> (32,64,100,etc), % samples (100, 50, ...), using the original
> (0.46875,0.46875,0.46875) and the unitary spacing, different number of grids
> (from 5 to 50) and the parameters of the LBFGSBOptimizer are the following:
>
>  double costFunctionconvergenceFactor = 1.e7;
>  double projectedGradientTolerance = 1e-6;
>  int maxNumberOfIterations = 500;
>  int maxNumberOfEvaluations = 500 ;
>  int maxNumberOfCorrections = 5;
>
> Among other tests, I've also changed the metric to MeanSquareMetric to know
> if mattes mutual information metric is the problem or, separately, the fixed
> and moving image have been binarized to reduce the information of each
> image. The output has been the same.
>
> All the tests seem to show that the problem is the optimizer, but I don't
> know how I can solve it because in some posts in the mailing list I've read
> that it is the best optimizer for deformable registration. Should I change
> it for another one (Regular or LBFGS)? Any other idea?
>
> Thanks in advance.
>
> Albert
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