[Insight-users] Affine registration and Viola-Wells MI

[Voigt, Markus]

Hello,

I'm trying to create a program where I want to use the Viola-Wells
(Mutual Information algorithm) and the affine registration.

In the examples of ITK I found the MI algorithm and the affine algorithm
but both are using different Optimizer. I tried to match all the things
together in one cpp-file so that it should work but it doesn't

I got this error message on the command line:

 "MutualInformationImageToImageMetric(018CBC40): All the sampled point
mapped to outside of the moving image"

Could someone help me? I think it is something simple, but I haven't got
a clue.

Cheers,

markus

 

 

This is the source code

 

  //start Viola-Wells mutual information

  metric->SetFixedImageStandardDeviation(  0.4 );

  metric->SetMovingImageStandardDeviation( 0.4 );

 

  //load pictures

  typedef itk::ImageFileReader< FixedImageType  > FixedImageReaderType;

  typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;

  FixedImageReaderType::Pointer  fixedImageReader  =
FixedImageReaderType::New();

  MovingImageReaderType::Pointer movingImageReader =
MovingImageReaderType::New();

  fixedImageReader->SetFileName(  arg1 );

  movingImageReader->SetFileName( arg2 );

  

  

  typedef itk::NormalizeImageFilter< 

                                FixedImageType, 

                                InternalImageType 

                                        > FixedNormalizeFilterType;

  typedef itk::NormalizeImageFilter< 

                                FixedImageType, 

                                InternalImageType 

                                              >
MovingNormalizeFilterType;

  FixedNormalizeFilterType::Pointer fixedNormalizer =
FixedNormalizeFilterType::New();

  MovingNormalizeFilterType::Pointer movingNormalizer =
MovingNormalizeFilterType::New();

 

 

  typedef itk::DiscreteGaussianImageFilter<

                                InternalImageType, 

                                InternalImageType

                                              > GaussianFilterType;

  GaussianFilterType::Pointer fixedSmoother  =
GaussianFilterType::New();

  GaussianFilterType::Pointer movingSmoother =
GaussianFilterType::New();

 

  fixedSmoother->SetVariance( 2.0 );

  movingSmoother->SetVariance( 2.0 );

 

  fixedNormalizer->SetInput(  fixedImageReader->GetOutput() );

  movingNormalizer->SetInput( movingImageReader->GetOutput() );

 

  fixedSmoother->SetInput( fixedNormalizer->GetOutput() );

  movingSmoother->SetInput( movingNormalizer->GetOutput() );

 

  registration->SetFixedImage( fixedSmoother->GetOutput() );

  registration->SetMovingImage( movingSmoother->GetOutput() );

 

  fixedNormalizer->Update();

  FixedImageType::RegionType fixedImageRegion =
fixedNormalizer->GetOutput()->GetBufferedRegion();

  registration->SetFixedImageRegion( fixedImageRegion );

  //end of Viola Wells MI

 

  //start with affine part

  typedef itk::CenteredTransformInitializer< TransformType,
FixedImageType,MovingImageType >  TransformInitializerType;

  TransformInitializerType::Pointer initializer =
TransformInitializerType::New();

  initializer->SetTransform(   transform );

  initializer->SetFixedImage(  fixedImageReader->GetOutput() );

  initializer->SetMovingImage( movingImageReader->GetOutput() );

  initializer->MomentsOn();

  initializer->InitializeTransform();

  

  registration->SetInitialTransformParameters(transform->GetParameters()
);

  double translationScale = 1.0 / 1000.0;

  unsigned int maxNumberOfIterations = atoi( arg7 );

 

  typedef OptimizerType::ScalesType       OptimizerScalesType;

  OptimizerScalesType optimizerScales(
transform->GetNumberOfParameters() );

 

  //rotation matrix factor

  optimizerScales[0] =  1.0;

  optimizerScales[1] =  1.0;

  //rotation center

  optimizerScales[2] =  1.0;

  optimizerScales[3] =  1.0;

  //components of the translation

  optimizerScales[4] =  translationScale;

  optimizerScales[5] =  translationScale;

  optimizer->SetScales( optimizerScales );

  

  

  optimizer->SetLearningRate( 15.0 );

  optimizer->SetNumberOfIterations( maxNumberOfIterations );

  optimizer->MaximizeOn();

 

  CommandIterationUpdate::Pointer observer =
CommandIterationUpdate::New();

  optimizer->AddObserver( itk::IterationEvent(), observer );

  //end with affine part

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