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

[Luis Ibanez]

Hi Deepak,

Just to answer your questions:

 > QUESTION:
 >
 > Now Im confused as to which optimizer to use for my problem
 >
 > 1. AmoebaOptimizer ( that is appropriate for
 > MatchCardinalityImageToImageMetric)


    Yes, the AmoebaOptimizer does not require the Metric
    to provide derivatives.


 > 2. VersorTransformOptimizer (that is appropriate for 
Similarity3DTransform)
 >
 > Also, does the VersorTransformOptimizer require analytical derivates of
 > the metric (which the MatchCardinalityImageToImageMetric does not 
provide).


     Yes, the VersorTransformOptimizer and its derived class
     the VersorRigid3DTransformOptimizer, both require the
     Metric class to provide Derivatives.




    Regards,


       Luis



-------------------------------
Deepak Roy Chittajallu 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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