[ITK] [ITK-users] 3D Affine Registration - no rotation found

Jerome Plumat j.plumat at auckland.ac.nz
Thu Jul 3 19:23:08 EDT 2014


Hi Nicolás,
Thank for the answer and advices. Indeed you are right: the value 1/1E7 
is too small. I set it to 1/1E2 and the registration is better 
performed. I also increased the number of samples but the scale value 
has a huge influence on the results.

However, I don't understand why this value has such an impact on the 
rotation optimization. If I understand well the user guide section 8.7.2 
top of page 359 the first optimizerScales[0] to 
optimizerScales[Dimension*Dimension-1] are relative to the moments of 
the affine transform while optimizerScales[Dimension*Dimension] to 
optimizerScales[(Dimensions+1)*Dimension-1] should influence the 
translations.

Any light?
Cheers,


Jerome Plumat
-------
School of Medical Sciences
University of Auckland


If I am not for myself, who will be for me? And if I am only for myself, then what an I? And if not now when? – Hillel HaZaken

On 03/07/14 20:12, Nicolas Gallego wrote:
> Hi Jérôme,
>
> Have you tried changing the optimizer scales?
> That parameter changes the sensitivity of the optimization process 
> between the translation and rotation of the transform, it seams to me 
> that the value 1.0/1e7 is probably too small and the optimizer is not 
> sensitive enough to rotations.
>
> For test purposes you can also set the mutual information metric to 
> compute histograms on the whole image, that may give you a much 
> detailed metric map for the optimizer as well.
>
> metric->SetNumberOfSpatialSamples(<some value>);
>
> Hope that helps,
>
> Nicolás Gallego-Ortiz
> Université catholique de Louvain, Belgium
>
>
> 2014-07-03 1:29 GMT+02:00 Jerome Plumat <j.plumat at auckland.ac.nz 
> <mailto:j.plumat at auckland.ac.nz>>:
>
>     Hi everyone,
>     I'm facing to a problem with 3D affine multi resolution registration.
>     The pipeline registers two 3D MRI head and necks volumes
>     (600x400x40). These are scans of the same patient at two times.
>     Due to the protocol, volumes are similar expect for MRI artifacts.
>     In fine the algorithm would be used to registered an atlas defined
>     on the fixed image on moving image.
>
>     The main elements of the pipeline are listed below. It's
>     implemented with itk4 and is mostly based on
>     MutliResImageRegistration2.cxx, I use the Mutual Information
>     metric, linear interpolator, the optimizer is
>     RegularStepGradientDescentOptimizer and CenteredTransformInitializer.
>
>     The registration is performed without any error and the
>     translation is correctly found but the algorithm can't provide any
>     rotation and I can't figure why.
>     The final results are
>     versor X = [1.00007, 0.000485777, 1.5912e-05]
>     versor Y = [-0.000126483, 0.999922, 1.34996e-05]
>     versor Z = [2.35376e-05, -8.28481e-05, 0.999986]
>     Translation X = 1.35204
>     Translation Y = 0.647584
>     Translation Z = -0.132708
>     Iterations = 96
>     Metric value = -0.738412
>
>     Following
>     http://www.cmake.org/pipermail/insight-users/2006-August/019025.html
>     the final rotation is 0.0003 along axis [-0.155456, -0.0122989,
>     -0.987766].
>     Moreover, visual validation clearly highlight the fact the
>     rotation is not sufficient.
>
>     Hope any one has advices.
>     Thanks in advances.
>
>     Jerome.
>
>
>     Part of code:
>
>     ....
>
>     // transform
>     typedef itk::AffineTransform< double, Dimension> TransformType;
>     typedef itk::CenteredTransformInitializer< TransformType,
>     FixedImageType, MovingImageType > TransformInitializerType;
>     // optimizer
>     typedef itk::RegularStepGradientDescentOptimizer OptimizerType;
>
>     // metric
>     typedef itk::MattesMutualInformationImageToImageMetric<
>     MovingImageType,
>     MovingImageType > MetricType;
>     // interpolator
>     typedef itk:: LinearInterpolateImageFunction<
>     MovingImageType,
>     double > InterpolatorType;
>
>     // registration
>     typedef itk::MultiResolutionImageRegistrationMethod<
>     FixedImageType,
>     MovingImageType > RegistrationType;
>
>
>     typedef itk::MultiResolutionPyramidImageFilter<
>     FixedImageType,
>     FixedImageType > FixedImagePyramidType;
>     typedef itk::MultiResolutionPyramidImageFilter<
>     MovingImageType,
>     MovingImageType > MovingImagePyramidType;
>
>
>     ...
>
>     metric->SetNumberOfHistogramBins( 256 );
>     metric->SetNumberOfSpatialSamples( 50000 );
>     metric->ReinitializeSeed( 76926294 <tel:%28%2076926294> );
>
>     ....
>
>     // connect the inputs
>     registration->SetFixedImage( fixedImage );
>     registration->SetMovingImage( movingImage );
>
>
>     // set the regions
>     registration->SetFixedImageRegion(
>     fixedImage->GetLargestPossibleRegion() );
>
>     // configure the initializer
>     initializer->SetTransform( transform );
>     initializer->SetFixedImage( fixedImage );
>     initializer->SetMovingImage( movingImage );
>     initializer->MomentsOn();
>     initializer->InitializeTransform();
>
>     registration->SetInitialTransformParameters(
>     transform->GetParameters() );
>
>     // optimizer scales
>     typedef OptimizerType::ScalesType OptimizerScalesType;
>     OptimizerScalesType optimizerScales(
>     transform->GetNumberOfParameters() );
>     const double momentScale = 1;
>     const double translationScale = 1.0 / 1e7;
>
>     //number of dimensions: N*(N+1)
>     for(unsigned int i=0; i<(Dimension*Dimension); i++ )
>     {
>     optimizerScales[i] = momentScale;
>     }
>
>     for( unsigned int i=(Dimension*Dimension);
>     i<((Dimension+1)*Dimension); i++ )
>     {
>     optimizerScales[i] = translationScale;
>     }
>     optimizer->SetScales( optimizerScales );
>
>
>     // performed registration
>     ...
>
>     //
>     // deformed the moving image and save it
>     //
>     typedef itk::ResampleImageFilter<
>     MovingImageType,
>     FixedImageType > ResampleFilterType;
>     typedef itk::CastImageFilter<
>     MovingImageType,
>     OutputImageType > CastFilterType;
>     typedef itk::ImageFileWriter< OutputImageType > WriterType;
>
>     ResampleFilterType::Pointer resampler = ResampleFilterType::New();
>     WriterType::Pointer writer = WriterType::New();
>     CastFilterType::Pointer caster = CastFilterType::New();
>
>     TransformType::Pointer finalTransform = TransformType::New();
>
>     finalTransform->SetParameters(
>     registration->GetLastTransformParameters() );
>     finalTransform->SetFixedParameters(
>     transform->GetFixedParameters() );
>
>     // set transform and initial moving image
>     resampler->SetTransform( finalTransform );
>     resampler->SetInput( movingImage );
>
>     resampler->SetSize(
>     fixedImage->GetLargestPossibleRegion().GetSize() );
>     resampler->SetOutputOrigin( fixedImage->GetOrigin() );
>     resampler->SetOutputSpacing( fixedImage->GetSpacing() );
>     resampler->SetOutputDirection( fixedImage->GetDirection() );
>     resampler->SetDefaultPixelValue( defaultOutputPixel );
>
>     writer->SetFileName( argv[argvOutputImageFile] );
>     caster->SetInput( resampler->GetOutput() );
>     writer->SetInput( caster->GetOutput() );
>
>     ...
>
>     -- 
>
>     Jerome
>     -------
>     School of Medical Sciences
>     University of Auckland
>
>     If I am not for myself, who will be for me? And if I am only for
>     myself, then what an I? And if not now when? – Hillel HaZaken
>
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