[Insight-users] Need Help --- Registration of Two Labelled Volumes !!!!

[Bill Lorensen]

You might try:
itkKappaStatisticImageToImageMetric

It does compute derivatives.

Bill

On Mon, Mar 16, 2009 at 1:09 PM, Deepak Roy Chittajallu
<cdeepakroy at gmail.com> wrote:
> Hello,
>
> PROBLEM:
>
> I am trying to perform registration of two labelled volumes using ITK. I
> intend to get it to work with 3D medical images.
>
> STUDY:
>
> I went through the Image Registration Section of the ITK software guide and
> I came to know that the appropriate metric for my problem is the
> itk::MatchCardinalityImageToImageMetric. I also went through the
> corresponding example in itk ---
> "Examples\Registration\ImageRegistration10.cxx".
>
> I found that the MatchCardinalityImageToImageMetric does not provide
> analytical derivatives and simply counts the number of matches/mismatches.
> Hence the regular optimizers wouldnt work, and hence i tried to use the
> AmoebaOptimizer as shown in the example.
>
> However i want to perform registration in the following order:  Similarity
> Transform, Affine, Deformable
>
> To start with, i am just trying it with the Similarity3DTransform.
>
> itk::Similarity3DTransform derives from the itk::VersorRigid3DTransform and
> i read in the ITK software guide that the most appropriate optimizer for
> this transform is itk::VersorTransformOptimizer as it computes the versor
> derivatives as defined by hamilton.
>
> QUESTION:
>
> Now Im confused as to which optimizer to use for my problem
>
> 1. AmoebaOptimizer ( that is appropriate for
> MatchCardinalityImageToImageMetric)
> 2. VersorTransformOptimizer (that is appropriate for Similarity3DTransform)
>
> Also, does the VersorTransformOptimizer require analytical derivates of the
> metric (which the MatchCardinalityImageToImageMetric does not provide).
>
> MY EXPERIMENTS:
>
> I tried to use the AmoebaOptimizer with the
> MatchCardinalityImageToImageMetric and Similarity3DTransform.
>
> I was not clear how to assign the Initial Simplex for my transform
> parameters particularly those of the quaternion. So i let the optimizer
> determine it automatically.
>
> However, the registration method fails with an exception.
>
> My Input test data (i created "simple" toy images to test my code):
>
>> Fixed Image: Stack of two 2D slices with a rectangle positioned at the
>> center of the image
>> Moving Image: Stack of two 2D slices with an ellipse (of smaller size than
>> a rectangle) position at the center of the image. The major axis of the
>> ellipse is at 45 degrees about the image x-axis.
>
> I think it is failing when the optimizer is working with the scaling
> parameter ... I dont see a reason why it should do so ....
>
> Below is the command line output:
>
> ****************************************************************************************
>
> Iter 1 : 0.1122 --- [0, 0, 0, 0, 0, 0, 1]
> Iter 2 : 0.117374 --- [0.00025, 0, 0, 0, 0, 0, 1]
> Iter 3 : 0.114343 --- [0, 0.00025, 0, 0, 0, 0, 1]
> Iter 4 : 0.114466 --- [0, 0, 0.00025, 0, 0, 0, 1]
> Iter 5 : 0.113333 --- [0, 0, 0, 0.00025, 0, 0, 1]
> Iter 6 : 0.113333 --- [0, 0, 0, 0, 0.00025, 0, 1]
> Iter 7 : 0.1122 --- [0, 0, 0, 0, 0, 0.00025, 1]
> ERROR-REGISTRATION :
> itk::ExceptionObject (013FFA90)
> Location: "double __thiscall itk::MatchCardinalityImageToImageMetric<class
> itk::Image<unsigned char,3>,class itk::Image<unsigned char,3>
>>::GetNonconstValue(const class itk::Array<double> &)"
> File:
> c:\itk\insighttoolkit-3.10.2\code\algorithms\itkMatchCardinalityImageToImageMetric.txx
> Line: 130
> Description: itk::ERROR: MatchCardinalityImageToImageMetric(018DCEE0): All
> the points mapped to outside of the moving image
>
> ***************************************************************************************************
>
> At the end of this message is a section of the code that i wrote ...
>
> Any help would be greatly appreciated.
>
> Thanks in advance.
>
> Regards,
>
> Deepak
>
>
> ****************************** CODE ******************************
>
> // Perform registration with Similarity3DTransform first
> typedef itk::ImageRegistrationMethod<FixedImageType, MovingImageType>
> RegistrationType;
> RegistrationType::Pointer pRegistrationMethod = RegistrationType::New();
>  // Metric
>  typedef
> itk::MatchCardinalityImageToImageMetric<FixedImageType,MovingImageType>
> MetricType;
>  MetricType::Pointer pMetric = MetricType::New();
>  pMetric->MeasureMatchesOff();
>  pRegistrationMethod->SetMetric( pMetric );
>
>  // Interpolator
>  typedef itk::NearestNeighborInterpolateImageFunction< MovingImageType,
> double> InterpolatorType;
>  InterpolatorType::Pointer pInterpolator = InterpolatorType::New();
>  pRegistrationMethod->SetInterpolator( pInterpolator );
>
>  // transform
>  typedef itk::Similarity3DTransform<double> SimilarityTransformType;
>  typedef
> itk::CenteredTransformInitializer<SimilarityTransformType,FixedImageType,MovingImageType>
> TransformInitializerType;
>
>  SimilarityTransformType::Pointer pTransform =
> SimilarityTransformType::New();
>  TransformInitializerType::Pointer pTransformInitializer =
> TransformInitializerType::New();
>
>  pTransformInitializer->SetFixedImage( pFixedImage );
>  pTransformInitializer->SetMovingImage( pMovingImage );
>  pTransformInitializer->SetTransform( pTransform );
>  pTransformInitializer->MomentsOn();
>  pTransformInitializer->InitializeTransform();
>
>   // rotation
>   typedef SimilarityTransformType::VersorType VersorType;
>   typedef VersorType::VectorType VectorType;
>   VectorType rot_axis;
>   rot_axis[0] = 0.0;
>   rot_axis[1] = 0.0;
>   rot_axis[2] = 1.0;
>   pTransform->SetRotation( rot_axis , 0.0 );
>
>  // scaling
>  pTransform->SetScale( 1.0 );
>  pRegistrationMethod->SetInitialTransformParameters(
> pTransform->GetParameters() );
>  pRegistrationMethod->SetTransform( pTransform );
>  // Optimizer
>  typedef itk::AmoebaOptimizer OptimizerType;
>  OptimizerType::Pointer pOptimizer = OptimizerType::New();
>  OptimizerType::ParametersType simplexDelta(
> pTransform->GetNumberOfParameters() );
> //   simplexDelta.Fill( 5.0 );
> //   pOptimizer->AutomaticInitialSimplexOff();
> //   pOptimizer->SetInitialSimplexDelta( simplexDelta );
>  pOptimizer->SetParametersConvergenceTolerance( 0.25 ); // quarter pixel
>  pOptimizer->SetFunctionConvergenceTolerance( 0.001 ); // 0.1%
>  pOptimizer->SetMaximumNumberOfIterations( 10 );
>  pRegistrationMethod->SetOptimizer( pOptimizer );
>
>  // Observer
>  typedef CommandIterationUpdate ObserverType;
>  ObserverType::Pointer pObserver = ObserverType::New();
>  pOptimizer->AddObserver( itk::IterationEvent() , pObserver);
>
> pRegistrationMethod->SetFixedImage( pFixedImage );
> pRegistrationMethod->SetMovingImage( pMovingImage );
> pRegistrationMethod->SetFixedImageRegion(
> pFixedImage->GetLargestPossibleRegion() );
>
> try
> {
>  pRegistrationMethod->Initialize();
>  pRegistrationMethod->StartRegistration();
> }
> catch(itk::ExceptionObject & err)
> {
>  std::cout << "\nERROR-REGISTRATION : " << err << std::endl;
>  return EXIT_FAILURE;
> }
>
> RegistrationType::ParametersType finalParameters;
> finalParameters = pRegistrationMethod->GetLastTransformParameters();
>
> std::cout << "Final: " << pMetric->GetValue( finalParameters ) << " --- " <<
> finalParameters << std::endl;
>
> // Resampling -- Transform the moving image using the obtained transform
> parameters
> typedef itk::ResampleImageFilter<MovingImageType,FixedImageType>
> ResampleFilterType;
> ResampleFilterType::Pointer pResampleImageFilter =
> ResampleFilterType::New();
>  // transform
>  SimilarityTransformType::Pointer pFinalTransform =
> SimilarityTransformType::New();
>  pFinalTransform->SetParameters(
> pRegistrationMethod->GetLastTransformParameters() );
> pResampleImageFilter->SetTransform( pFinalTransform );
> pResampleImageFilter->SetInput( pMovingImage );
> pResampleImageFilter->SetSize(
> pFixedImage->GetLargestPossibleRegion().GetSize() );
> pResampleImageFilter->SetOutputOrigin(  pFixedImage->GetOrigin() );
> pResampleImageFilter->SetOutputSpacing( pFixedImage->GetSpacing() );
> pResampleImageFilter->SetOutputDirection( pFixedImage->GetDirection() );
> pResampleImageFilter->SetDefaultPixelValue( 0 );
> pResampleImageFilter->SetInterpolator( pInterpolator );
> pResampleImageFilter->Update();
>
> ************************************************************************************************
>
>
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