CMatrix4x4 GlobalAdaptation::adaptation(bool itkAmoeba)
{
	unsigned int numberOfParameters;
	if (mode_ == "rotation")
		numberOfParameters = 3;
	else if (mode_ == "rotation+scaling")
		numberOfParameters = 4;
	else if (mode_ == "rotation+translation")
		numberOfParameters = 6;
	vector<double> p;
	if (!itkAmoeba) { // can only be used with rotation
		double	mean1 = -0.00924, std1 = 0.057,
				mean2 = -0.0022, std2 = 0.0615,
				mean3 = 0.0140, std3 = 0.0427;
		OptimizerType::BoundSelectionType boundSelect(3);
		boundSelect.Fill( 2 );
		OptimizerType::BoundValueType upperBound(3);
		upperBound[0] = mean1+3*std1; upperBound[1] = mean2+3*std2; upperBound[2] = mean3+3*std3;
		OptimizerType::BoundValueType lowerBound(3);
		lowerBound[0] = mean1-3*std1; lowerBound[1] = mean2-3*std2; lowerBound[2] = mean3-3*std3;

		FoncteurGlobalAdaptation* f = new FoncteurGlobalAdaptation(image_,mesh_->getListTrianglesBarycentre(),numberOfParameters);
		f->setPointRotation(pointRotation_);

		OptimizerType::ParametersType pInit(3);
		pInit.Fill(0.0);

		OptimizerType::Pointer LBFGSBOptimizer = OptimizerType::New();
		LBFGSBOptimizer->SetCostFunction(f);
		LBFGSBOptimizer->SetBoundSelection( boundSelect );
		LBFGSBOptimizer->SetUpperBound( upperBound );
		LBFGSBOptimizer->SetLowerBound( lowerBound );
		LBFGSBOptimizer->SetCostFunctionConvergenceFactor( 1.e7 );
		LBFGSBOptimizer->SetProjectedGradientTolerance( 1e-35);
		LBFGSBOptimizer->SetMaximumNumberOfIterations( 250 );
		LBFGSBOptimizer->SetMaximumNumberOfEvaluations( 250 );
		LBFGSBOptimizer->SetMaximumNumberOfCorrections( 7 );
		LBFGSBOptimizer->SetInitialPosition(pInit);

		try {
			LBFGSBOptimizer->StartOptimization();
		}
		catch ( std::bad_alloc & err ) {
			cerr << "BadAlloc caught !" << (char*)err.what() << endl;
		}
		catch( itk::ExceptionObject & err ) {
			cerr << "ExceptionObject caught !" << err << endl;
		}

		OptimizerType::ParametersType pFinal = LBFGSBOptimizer->GetCurrentPosition();

		cout << LBFGSBOptimizer->GetStopConditionDescription() << endl;

		for (int i=0; i<pFinal.size(); i++)
			p.push_back(pFinal[i]);
	}
	else {
		// Utilisation de la méthode du Simplex implémentée dans la librairie ITK
		itk::AmoebaOptimizer::Pointer optimizer = itk::AmoebaOptimizer::New();
	
		SCRegion* region = new SCRegion();
		int *sizeDesired = new int[3];
		double *spacingDesired = new double[3];
		sizeDesired[0] = 61; sizeDesired[1] = 61; sizeDesired[2] = 81;
		spacingDesired[0] = 1; spacingDesired[1] = 1; spacingDesired[2] = 1;
		region->setSize(sizeDesired);
		region->setSpacing(spacingDesired);
		region->setOrigin(pointRotation_[0],pointRotation_[1],pointRotation_[2]);
		region->setNormal(normal_mesh_[0],normal_mesh_[1],normal_mesh_[2]);
		region->setFactor(image_->getTypeImageFactor());
		region->build2DGaussian(1.5);

		FoncteurGlobalAdaptation* f = new FoncteurGlobalAdaptation(image_,mesh_->getListTrianglesBarycentre(),numberOfParameters);
		f->setPointRotation(pointRotation_);
		f->setGaussianRegion(region);
		optimizer->SetCostFunction(f);
		optimizer->SetOptimizeWithRestarts(false);
		optimizer->SetMaximumNumberOfIterations(250);
		optimizer->AutomaticInitialSimplexOn();
		
		ParametersType pInit, pFinal;
		//pInit.SetSize(6);
		double* init = new double[numberOfParameters];
		for (int i=0; i<numberOfParameters; i++) init[i] = 0.0;
		if (mode_ == "rotation+scaling")
			init[3] = 1.0;
		pInit.SetData(init,numberOfParameters,false);
		optimizer->SetFunctionConvergenceTolerance(1.0e-4);
		optimizer->SetParametersConvergenceTolerance(1.0e-8);
		optimizer->SetInitialPosition(pInit);
		try {
			optimizer->StartOptimization();
		}
		catch ( std::bad_alloc & err ) {
			cerr << "BadAlloc caught !" << (char*)err.what() << endl;
		}
		catch( itk::ExceptionObject & err ) {
			cerr << "ExceptionObject caught !" << err << endl;
		}
		//cout << optimizer->GetStopConditionDescription() << endl << optimizer->GetValue() << endl;
		pFinal = optimizer->GetCurrentPosition();
		for (int i=0; i<pFinal.size(); i++)
			p.push_back(pFinal[i]);
	}

	/*cout << "Affichage des resultats..." << endl;
	for (int i=0; i<p.size(); i++)
		cout << i << " " << p[i] << endl << endl;*/

	CMatrix4x4 transformation;
	double	mean1 = 0.0044, std1 = 0.0868,
			mean2 = -0.0107, std2 = 0.1170,
			mean3 = 0.0110, std3 = 0.1495;
	double factor = 2.7;
	if (p[0]<=mean1+factor*std1 && p[0]>=mean1-factor*std1 && p[1]<=mean2+factor*std2 && p[1]>=mean2-factor*std2 && p[2]<=mean3+factor*std3 && p[2]>=mean3-factor*std3)
	{
		transformation[0] = cos(p[0])*cos(p[1]),	transformation[4] = -cos(p[2])*sin(p[1]) + sin(p[2])*sin(p[0])*cos(p[1]),	transformation[8] = sin(p[2])*sin(p[1]) + cos(p[2])*sin(p[0])*cos(p[1]),
		transformation[1] = cos(p[0])*sin(p[1]),	transformation[5] = cos(p[2])*cos(p[1]) + sin(p[2])*sin(p[0])*sin(p[1]),	transformation[9] = -sin(p[2])*cos(p[1]) + cos(p[2])*sin(p[0])*sin(p[1]),
		transformation[2] = -sin(p[0]),				transformation[6] = sin(p[2])*cos(p[0]),									transformation[10] = cos(p[2])*cos(p[0]);
		if (mode_ == "rotation+translation") {
			transformation[12] = p[3], transformation[13] = p[4], transformation[14] = p[5];
		}
		if (mode_ == "rotation+scaling") {
			transformation[0] *= p[3];
			transformation[5] *= p[3];
			transformation[10] *= p[3];
		}
		//cout << transformation << endl;

		mesh_->transform(transformation,pointRotation_);
		badOrientation_ = false;
	}
	else
	{
		/*if (verbose_) {
            cout << "Rotations : " << endl;
            cout << mean1-factor*std1 << " <= " << p[0] << " <= " << mean1+factor*std1 << endl;
            cout << mean2-factor*std2 << " <= " << p[1] << " <= " << mean2+factor*std2 << endl;
            cout << mean3-factor*std3 << " <= " << p[2] << " <= " << mean3+factor*std3 << endl;
            cout << "Rotation Index Error :";
            if (p[0]>=mean1+factor*std1 || p[0]<=mean1-factor*std1) cout << " 0";
            if (p[1]>=mean2+factor*std2 || p[1]<=mean2-factor*std2) cout << " 1";
            if (p[2]>=mean3+factor*std3 || p[2]<=mean3-factor*std3) cout << " 2";
            cout << endl;
        }//*/
		badOrientation_ = true;
	}

	return transformation;
}
示例#2
0
// perform B-spline registration for 2D image
void runBspline2D(StringVector& args) {
    typedef itk::BSplineTransform<double, 2, 3> TransformType;
    typedef itk::LBFGSOptimizer OptimizerType;
    typedef itk::MeanSquaresImageToImageMetric<RealImage2, RealImage2> MetricType;
    typedef itk:: LinearInterpolateImageFunction<RealImage2, double> InterpolatorType;
    typedef itk::ImageRegistrationMethod<RealImage2, RealImage2> RegistrationType;

    MetricType::Pointer         metric        = MetricType::New();
    OptimizerType::Pointer      optimizer     = OptimizerType::New();
    InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
    RegistrationType::Pointer   registration  = RegistrationType::New();

    // The old registration framework has problems with multi-threading
    // For now, we set the number of threads to 1
    registration->SetNumberOfThreads(1);

    registration->SetMetric(        metric        );
    registration->SetOptimizer(     optimizer     );
    registration->SetInterpolator(  interpolator  );

    TransformType::Pointer  transform = TransformType::New();
    registration->SetTransform( transform );


    ImageIO<RealImage2> io;

    // Create the synthetic images
    RealImage2::Pointer  fixedImage  = io.ReadImage(args[0]);
    RealImage2::Pointer  movingImage  = io.ReadImage(args[1]);

    // Setup the registration
    registration->SetFixedImage(  fixedImage   );
    registration->SetMovingImage(   movingImage);

    RealImage2::RegionType fixedRegion = fixedImage->GetBufferedRegion();
    registration->SetFixedImageRegion( fixedRegion );

    TransformType::PhysicalDimensionsType   fixedPhysicalDimensions;
    TransformType::MeshSizeType             meshSize;
    for( unsigned int i=0; i < 2; i++ )
    {
        fixedPhysicalDimensions[i] = fixedImage->GetSpacing()[i] *
        static_cast<double>(
                            fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1 );
    }
    unsigned int numberOfGridNodesInOneDimension = 18;
    meshSize.Fill( numberOfGridNodesInOneDimension - 3 );
    transform->SetTransformDomainOrigin( fixedImage->GetOrigin() );
    transform->SetTransformDomainPhysicalDimensions( fixedPhysicalDimensions );
    transform->SetTransformDomainMeshSize( meshSize );
    transform->SetTransformDomainDirection( fixedImage->GetDirection() );

    typedef TransformType::ParametersType     ParametersType;

    const unsigned int numberOfParameters =
    transform->GetNumberOfParameters();

    ParametersType parameters( numberOfParameters );

    parameters.Fill( 0.0 );

    transform->SetParameters( parameters );

    //  We now pass the parameters of the current transform as the initial
    //  parameters to be used when the registration process starts.

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

    std::cout << "Intial Parameters = " << std::endl;
    std::cout << transform->GetParameters() << std::endl;

    //  Next we set the parameters of the LBFGS Optimizer.

    optimizer->SetGradientConvergenceTolerance( 0.005 );
    optimizer->SetLineSearchAccuracy( 0.9 );
    optimizer->SetDefaultStepLength( .1 );
    optimizer->TraceOn();
    optimizer->SetMaximumNumberOfFunctionEvaluations( 1000 );

    std::cout << std::endl << "Starting Registration" << std::endl;

    try
    {
        registration->Update();
        std::cout << "Optimizer stop condition = "
        << registration->GetOptimizer()->GetStopConditionDescription()
        << std::endl;
    }
    catch( itk::ExceptionObject & err )
    {
        std::cerr << "ExceptionObject caught !" << std::endl;
        std::cerr << err << std::endl;
        return;
    }

    OptimizerType::ParametersType finalParameters =
    registration->GetLastTransformParameters();

    std::cout << "Last Transform Parameters" << std::endl;
    std::cout << finalParameters << std::endl;

    transform->SetParameters( finalParameters );

    typedef itk::ResampleImageFilter<RealImage2, RealImage2> ResampleFilterType;

    ResampleFilterType::Pointer resample = ResampleFilterType::New();

    resample->SetTransform( transform );
    resample->SetInput( movingImage );

    resample->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
    resample->SetOutputOrigin(  fixedImage->GetOrigin() );
    resample->SetOutputSpacing( fixedImage->GetSpacing() );
    resample->SetOutputDirection( fixedImage->GetDirection() );
    resample->SetDefaultPixelValue( 100 );
    resample->Update();

    io.WriteImage(args[2], resample->GetOutput());
}
    void BSplineRegistration::GenerateData2( itk::Image<TPixel, VImageDimension>* itkImage1)
  {
    std::cout << "start bspline registration" << std::endl;
    
    // Typedefs
    typedef typename itk::Image< TPixel, VImageDimension >  InternalImageType;
       
    typedef typename itk::Vector< float, VImageDimension >    VectorPixelType;
    typedef typename itk::Image<  VectorPixelType, VImageDimension > DeformationFieldType;

    typedef itk::BSplineDeformableTransform<
                                double,
                                VImageDimension,
                                3 >                         TransformType;

    typedef typename TransformType::ParametersType          ParametersType;



    
    //typedef itk::LBFGSOptimizer                             OptimizerType;
    typedef itk::SingleValuedNonLinearOptimizer             OptimizerType;
    //typedef itk::SingleValuedCostFunction                   MetricType;

    typedef itk::MattesMutualInformationImageToImageMetric<
                                InternalImageType,
                                InternalImageType >           MetricType;

    typedef itk::MeanSquaresImageToImageMetric<
                                InternalImageType,
                                InternalImageType >           MetricTypeMS;

    typedef itk::LinearInterpolateImageFunction<
                                InternalImageType,
                                double >                    InterpolatorType;

    typedef itk::ImageRegistrationMethod<
                                InternalImageType,
                                InternalImageType >           RegistrationType;

    typedef typename itk::WarpImageFilter<
                            InternalImageType, 
                            InternalImageType,
                            DeformationFieldType  >         WarperType;

    typedef typename TransformType::SpacingType                      SpacingType;

    typedef typename TransformType::OriginType                       OriginType;

    typedef itk::ResampleImageFilter< 
                                InternalImageType, 
                                InternalImageType >            ResampleFilterType;

    typedef itk::Image< TPixel, VImageDimension >           OutputImageType;
  

    // Sample new image with the same image type as the fixed image
    typedef itk::CastImageFilter< 
                                InternalImageType,
                                InternalImageType >            CastFilterType;
                    
    
    typedef itk::Vector< float, VImageDimension >           VectorType;
    typedef itk::Image< VectorType, VImageDimension >       DeformationFieldType;


    typedef itk::BSplineDeformableTransformInitializer <
                                TransformType,
                                InternalImageType >            InitializerType;
    

    typename InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
    typename RegistrationType::Pointer   registration  = RegistrationType::New();   
    typename InitializerType::Pointer    initializer   = InitializerType::New();
    typename TransformType::Pointer      transform     = TransformType::New();
    
    
    if(m_Metric==0 || m_Metric==1)
    {
      typename MetricType::Pointer metric = MetricType::New();
      metric->SetNumberOfHistogramBins( 32);
      metric->SetNumberOfSpatialSamples(90000);
      registration->SetMetric(          metric       );
    }
    else{
      typename MetricTypeMS::Pointer metric = MetricTypeMS::New();  
      registration->SetMetric(          metric       );
    }
     
    typename OptimizerFactory::Pointer optFac = OptimizerFactory::New();
    optFac->SetOptimizerParameters(m_OptimizerParameters);
    optFac->SetNumberOfTransformParameters(transform->GetNumberOfParameters());
    OptimizerType::Pointer optimizer = optFac->GetOptimizer();    

    optimizer->AddObserver(itk::AnyEvent(), m_Observer);
    
    //typedef mitk::MetricFactory <TPixel, VImageDimension> MetricFactoryType;
    //typename MetricFactoryType::Pointer metricFac = MetricFactoryType::New();
    //metricFac->SetMetricParameters(m_MetricParameters);
    ////MetricType::Pointer metric = metricFac->GetMetric();


    typename InternalImageType::Pointer fixedImage = InternalImageType::New();
    mitk::CastToItkImage(m_ReferenceImage, fixedImage);    
    typename InternalImageType::Pointer movingImage = itkImage1;
    typename InternalImageType::RegionType fixedRegion = fixedImage->GetBufferedRegion();
    typename InternalImageType::RegionType movingRegion = movingImage->GetBufferedRegion();

    
    if(m_MatchHistograms)
    {
      typedef itk::RescaleIntensityImageFilter<InternalImageType,InternalImageType> FilterType;   
      typedef itk::HistogramMatchingImageFilter<InternalImageType,InternalImageType> HEFilterType;

      typename FilterType::Pointer inputRescaleFilter = FilterType::New();  
      typename FilterType::Pointer referenceRescaleFilter = FilterType::New();  

      referenceRescaleFilter->SetInput(fixedImage);
      inputRescaleFilter->SetInput(movingImage);

      TPixel desiredMinimum =  0;
      TPixel desiredMaximum =  255;
      
      referenceRescaleFilter->SetOutputMinimum( desiredMinimum );
      referenceRescaleFilter->SetOutputMaximum( desiredMaximum );
      referenceRescaleFilter->UpdateLargestPossibleRegion();  
      inputRescaleFilter->SetOutputMinimum( desiredMinimum );
      inputRescaleFilter->SetOutputMaximum( desiredMaximum );
      inputRescaleFilter->UpdateLargestPossibleRegion();

      // Histogram match the images
      typename HEFilterType::Pointer intensityEqualizeFilter = HEFilterType::New();

      intensityEqualizeFilter->SetReferenceImage( inputRescaleFilter->GetOutput() );
      intensityEqualizeFilter->SetInput( referenceRescaleFilter->GetOutput() );
      intensityEqualizeFilter->SetNumberOfHistogramLevels( 64 );
      intensityEqualizeFilter->SetNumberOfMatchPoints( 12 );
      intensityEqualizeFilter->ThresholdAtMeanIntensityOn();
      intensityEqualizeFilter->Update();

      //fixedImage = referenceRescaleFilter->GetOutput();
      //movingImage = IntensityEqualizeFilter->GetOutput();

      fixedImage = intensityEqualizeFilter->GetOutput();
      movingImage = inputRescaleFilter->GetOutput();
    }


    //
    registration->SetOptimizer(       optimizer     );
    registration->SetInterpolator(    interpolator  );  
    registration->SetFixedImage(      fixedImage    );
    registration->SetMovingImage(     movingImage   );    
    registration->SetFixedImageRegion(fixedRegion   );

    initializer->SetTransform(transform);
    initializer->SetImage(fixedImage);
    initializer->SetNumberOfGridNodesInsideTheImage( m_NumberOfGridPoints );
    initializer->InitializeTransform();

    registration->SetTransform( transform );    

    const unsigned int numberOfParameters = transform->GetNumberOfParameters();
    
    typename itk::BSplineDeformableTransform<
                                double,
                                VImageDimension,
                                3 >::ParametersType  parameters;

    parameters.set_size(numberOfParameters);
    parameters.Fill( 0.0 );
    transform->SetParameters( parameters );

    // We now pass the parameters of the current transform as the initial
    // parameters to be used when the registration process starts.
    registration->SetInitialTransformParameters( transform->GetParameters() );
    
    std::cout << "Intial Parameters = " << std::endl;
    std::cout << transform->GetParameters() << std::endl;
 

    std::cout << std::endl << "Starting Registration" << std::endl;

    try 
    { 
      double tstart(clock());     
      registration->StartRegistration();    
      double time = clock() - tstart;
      time = time / CLOCKS_PER_SEC;
      MITK_INFO << "Registration time: " << time;
    } 
    catch( itk::ExceptionObject & err ) 
    { 
      std::cerr << "ExceptionObject caught !" << std::endl; 
      std::cerr << err << std::endl;       
    } 
    
    typename OptimizerType::ParametersType finalParameters = 
                      registration->GetLastTransformParameters();

    std::cout << "Last Transform Parameters" << std::endl;
    std::cout << finalParameters << std::endl;

    transform->SetParameters( finalParameters );

/*
    ResampleFilterType::Pointer       resampler = ResampleFilterType::New();
    resampler->SetTransform(          transform );
    resampler->SetInput(              movingImage );
    resampler->SetSize(               fixedImage->GetLargestPossibleRegion().GetSize() );
    resampler->SetOutputOrigin(       fixedImage->GetOrigin() );
    resampler->SetOutputSpacing(      fixedImage->GetSpacing() );
    resampler->SetOutputDirection(    fixedImage->GetDirection() );
    resampler->SetDefaultPixelValue(  100 );
    resampler->SetInterpolator(       interpolator);
    resampler->Update();*/



    // Generate deformation field
    typename DeformationFieldType::Pointer field = DeformationFieldType::New();    
    field->SetRegions( movingRegion );
    field->SetOrigin( movingImage->GetOrigin() );
    field->SetSpacing( movingImage->GetSpacing() );
    field->SetDirection( movingImage->GetDirection() );   
    field->Allocate();


    typedef itk::ImageRegionIterator< DeformationFieldType > FieldIterator;
    FieldIterator fi( field, movingRegion );
    fi.GoToBegin();

    typename TransformType::InputPointType  fixedPoint;
    typename TransformType::OutputPointType movingPoint;
    typename DeformationFieldType::IndexType index;

    VectorType displacement;

    while( ! fi.IsAtEnd() )
    {
      index = fi.GetIndex();
      field->TransformIndexToPhysicalPoint( index, fixedPoint );
      movingPoint = transform->TransformPoint( fixedPoint );
      displacement = movingPoint - fixedPoint;
      fi.Set( displacement );
      ++fi;
    }


    // Use the deformation field to warp the moving image
    typename WarperType::Pointer warper = WarperType::New();    
    warper->SetInput( movingImage );
    warper->SetInterpolator( interpolator );
    warper->SetOutputSpacing( movingImage->GetSpacing() );
    warper->SetOutputOrigin( movingImage->GetOrigin() );
    warper->SetOutputDirection( movingImage->GetDirection() );
    warper->SetDeformationField( field );
    warper->Update();

    typename InternalImageType::Pointer result = warper->GetOutput();    

    if(m_UpdateInputImage)
    {   
      Image::Pointer outputImage = this->GetOutput();
      mitk::CastToMitkImage( result, outputImage );
    }


    // Save the deformationfield resulting from the registration    
    if(m_SaveDeformationField)
    {      
      typedef itk::ImageFileWriter< DeformationFieldType >  FieldWriterType;
      typename FieldWriterType::Pointer fieldWriter = FieldWriterType::New();

      fieldWriter->SetInput( field );
      
      fieldWriter->SetFileName( m_DeformationFileName );
      try
      {
        fieldWriter->Update();
      }
      catch( itk::ExceptionObject & excp )
      {
        std::cerr << "Exception thrown " << std::endl;
        std::cerr << excp << std::endl;
        //return EXIT_FAILURE;
      }
    }



  }
void MutualInformationRegistration::updateRegistration() {
    if(!isReady())
        return;

    convertVolumes();

    //typedef itk::RegularStepGradientDescentOptimizer OptimizerType;
    typedef itk::VersorRigid3DTransformOptimizer OptimizerType;
    typedef OptimizerType::ScalesType       OptimizerScalesType;
    typedef itk::LinearInterpolateImageFunction< InternalImageType, double             > InterpolatorType;
    typedef itk::MattesMutualInformationImageToImageMetric< InternalImageType, InternalImageType >   MetricType;
    typedef itk::MultiResolutionImageRegistrationMethod< InternalImageType, InternalImageType >   RegistrationType;

    typedef itk::MultiResolutionPyramidImageFilter< InternalImageType, InternalImageType >   FixedImagePyramidType;
    typedef itk::MultiResolutionPyramidImageFilter< InternalImageType, InternalImageType >   MovingImagePyramidType;

    OptimizerType::Pointer      optimizer     = OptimizerType::New();
    InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
    RegistrationType::Pointer   registration  = RegistrationType::New();
    MetricType::Pointer         metric        = MetricType::New();

    FixedImagePyramidType::Pointer fixedImagePyramid = FixedImagePyramidType::New();
    MovingImagePyramidType::Pointer movingImagePyramid = MovingImagePyramidType::New();

    registration->SetOptimizer(optimizer);
    registration->SetTransform(transform_);
    registration->SetInterpolator(interpolator);
    registration->SetMetric(metric);
    registration->SetFixedImagePyramid(fixedImagePyramid);
    registration->SetMovingImagePyramid(movingImagePyramid);

    OptimizerScalesType optimizerScales( transform_->GetNumberOfParameters() );

    float rotScale = 1.0 / 1000.0f;
    optimizerScales[0] = 1.0f;
    optimizerScales[1] = 1.0f;
    optimizerScales[2] = 1.0f;
    optimizerScales[3] = rotScale;
    optimizerScales[4] = rotScale;
    optimizerScales[5] = rotScale;
    optimizer->SetScales( optimizerScales );
    optimizer->SetMaximumStepLength(0.2);
    optimizer->SetMinimumStepLength(0.0001);

    InternalImageType::Pointer fixed = voreenToITK<float>(fixedVolumeFloat_);
    InternalImageType::Pointer moving = voreenToITK<float>(movingVolumeFloat_);
    registration->SetFixedImage(fixed);
    registration->SetMovingImage(moving);
    registration->SetFixedImageRegion( fixed->GetBufferedRegion() );

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

    metric->SetNumberOfHistogramBins(numHistogramBins_.get());

    size_t numVoxels = hmul(fixedVolumeFloat_->getDimensions());
    metric->SetNumberOfSpatialSamples(numVoxels * numSamples_.get());

    metric->ReinitializeSeed( 76926294 );

    //// Define whether to calculate the metric derivative by explicitly
    //// computing the derivatives of the joint PDF with respect to the Transform
    //// parameters, or doing it by progressively accumulating contributions from
    //// each bin in the joint PDF.
    metric->SetUseExplicitPDFDerivatives(explicitPDF_.get());

    optimizer->SetNumberOfIterations(numIterations_.get());

    optimizer->SetRelaxationFactor(relaxationFactor_.get());

    // Create the Command observer and register it with the optimizer.
    CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
    optimizer->AddObserver( itk::IterationEvent(), observer );

    typedef RegistrationInterfaceCommand<RegistrationType> CommandType;
    CommandType::Pointer command = CommandType::New();
    registration->AddObserver( itk::IterationEvent(), command );

    registration->SetNumberOfLevels(numLevels_.get());

    try
    {
        registration->StartRegistration();
        std::cout << "Optimizer stop condition: " << registration->GetOptimizer()->GetStopConditionDescription() << std::endl;
    }
    catch( itk::ExceptionObject & err )
    {
        std::cout << "ExceptionObject caught !" << std::endl;
        std::cout << err << std::endl;
        //return EXIT_FAILURE;
    }

    ParametersType finalParameters = registration->GetLastTransformParameters();
    transform_->SetParameters(finalParameters);

    unsigned int numberOfIterations = optimizer->GetCurrentIteration();

    double bestValue = optimizer->GetValue();

    // Print out results
    std::cout << "Result = " << std::endl;
    std::cout << " Versor " << finalParameters[0] << " " << finalParameters[1] << " " << finalParameters[2] << std::endl;
    std::cout << " Translation " << finalParameters[1] << " " << finalParameters[4] << " " << finalParameters[5] << std::endl;
    std::cout << " Iterations    = " << numberOfIterations << std::endl;
    std::cout << " Metric value  = " << bestValue          << std::endl;

    invalidate(INVALID_RESULT);
}
示例#5
0
    RealImage::Pointer bsplineRegistration(RealImage::Pointer srcImg, RealImage::Pointer dstImg) {

        const unsigned int SpaceDimension = ImageDimension;
        const unsigned int SplineOrder = 3;
        typedef double CoordinateRepType;

        typedef itk::BSplineTransform<CoordinateRepType, SpaceDimension, SplineOrder> TransformType;
        typedef itk::LBFGSOptimizer OptimizerType;
        typedef itk::MeanSquaresImageToImageMetric<ImageType, ImageType> MetricType;
        typedef itk::LinearInterpolateImageFunction<ImageType, double> InterpolatorType;
        typedef itk::ImageRegistrationMethod<ImageType, ImageType> RegistrationType;

        MetricType::Pointer         metric        = MetricType::New();
        OptimizerType::Pointer      optimizer     = OptimizerType::New();
        InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
        RegistrationType::Pointer   registration  = RegistrationType::New();



        // The old registration framework has problems with multi-threading
        // For now, we set the number of threads to 1
//        registration->SetNumberOfThreads(1);
        registration->SetMetric(        metric        );
        registration->SetOptimizer(     optimizer     );
        registration->SetInterpolator(  interpolator  );

        TransformType::Pointer  transform = TransformType::New();
        registration->SetTransform( transform );

        // Setup the registration
        registration->SetFixedImage(  dstImg   );
        registration->SetMovingImage(   srcImg);

        ImageType::RegionType fixedRegion = srcImg->GetBufferedRegion();
        registration->SetFixedImageRegion( fixedRegion );

        //  Here we define the parameters of the BSplineDeformableTransform grid.  We
        //  arbitrarily decide to use a grid with $5 \times 5$ nodes within the image.
        //  The reader should note that the BSpline computation requires a
        //  finite support region ( 1 grid node at the lower borders and 2
        //  grid nodes at upper borders). Therefore in this example, we set
        //  the grid size to be $8 \times 8$ and place the grid origin such that
        //  grid node (1,1) coincides with the first pixel in the fixed image.

        TransformType::PhysicalDimensionsType   fixedPhysicalDimensions;
        TransformType::MeshSizeType             meshSize;
        for (unsigned int i=0; i < ImageDimension; i++) {
            fixedPhysicalDimensions[i] = dstImg->GetSpacing()[i] *
            static_cast<double>(dstImg->GetLargestPossibleRegion().GetSize()[i] - 1 );
            meshSize[i] = dstImg->GetLargestPossibleRegion().GetSize()[i] / 8 - SplineOrder;
        }
//        unsigned int numberOfGridNodesInOneDimension = 15;
//        meshSize.Fill( numberOfGridNodesInOneDimension - SplineOrder );
        transform->SetTransformDomainOrigin( dstImg->GetOrigin() );
        transform->SetTransformDomainPhysicalDimensions( fixedPhysicalDimensions );
        transform->SetTransformDomainMeshSize( meshSize );
        transform->SetTransformDomainDirection( dstImg->GetDirection() );

        typedef TransformType::ParametersType     ParametersType;

        const unsigned int numberOfParameters = transform->GetNumberOfParameters();

        ParametersType parameters( numberOfParameters );
        parameters.Fill( 0.0 );

        transform->SetParameters( parameters );

        //  We now pass the parameters of the current transform as the initial
        //  parameters to be used when the registration process starts.

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

        std::cout << "Intial Parameters = " << std::endl;
        std::cout << transform->GetParameters() << std::endl;

        //  Next we set the parameters of the LBFGS Optimizer.
        optimizer->SetGradientConvergenceTolerance(0.1);
        optimizer->SetLineSearchAccuracy(0.09);
        optimizer->SetDefaultStepLength(.1);
        optimizer->TraceOn();
        optimizer->SetMaximumNumberOfFunctionEvaluations(1000);

        std::cout << std::endl << "Starting Registration" << std::endl;

        try {
            registration->Update();
            std::cout << "Optimizer stop condition = "
            << registration->GetOptimizer()->GetStopConditionDescription()
            << std::endl;
        } catch (itk::ExceptionObject & err) {
            std::cerr << "ExceptionObject caught !" << std::endl;
            std::cerr << err << std::endl;
            return RealImage::Pointer();
        }

        OptimizerType::ParametersType finalParameters =
        registration->GetLastTransformParameters();
        
        std::cout << "Last Transform Parameters" << std::endl;
        std::cout << finalParameters << std::endl;
        
        transform->SetParameters( finalParameters );
        
        typedef itk::ResampleImageFilter<ImageType, ImageType>    ResampleFilterType;
        
        ResampleFilterType::Pointer resample = ResampleFilterType::New();
        
        resample->SetTransform( transform );
        resample->SetInput( srcImg );
        
        resample->SetSize(    dstImg->GetLargestPossibleRegion().GetSize() );
        resample->SetOutputOrigin(  dstImg->GetOrigin() );
        resample->SetOutputSpacing( dstImg->GetSpacing() );
        resample->SetOutputDirection( dstImg->GetDirection() );
        resample->SetDefaultPixelValue( 100 );
        resample->Update();
        return resample->GetOutput();
    }
typename TImage::Pointer modelBasedImageToImageRegistration(std::string referenceFilename,
																					std::string targetFilename, 
																					typename TStatisticalModelType::Pointer model,
																					std::string outputDfFilename,
																					unsigned numberOfIterations){

	typedef itk::ImageFileReader<TImage> ImageReaderType;
	typedef itk::InterpolatingStatisticalDeformationModelTransform<TRepresenter, double, VImageDimension> TransformType;
	typedef itk::LBFGSOptimizer OptimizerType;
	typedef itk::ImageRegistrationMethod<TImage, TImage> RegistrationFilterType;
	typedef itk::WarpImageFilter< TImage, TImage, TVectorImage > WarperType;
	typedef itk::LinearInterpolateImageFunction< TImage, double > InterpolatorType;
	
	typename ImageReaderType::Pointer referenceReader = ImageReaderType::New();
	referenceReader->SetFileName(referenceFilename.c_str());
	referenceReader->Update();
	typename TImage::Pointer referenceImage = referenceReader->GetOutput();
	referenceImage->Update();

	typename ImageReaderType::Pointer targetReader = ImageReaderType::New();
	targetReader->SetFileName(targetFilename.c_str());
	targetReader->Update();
	typename TImage::Pointer targetImage = targetReader->GetOutput();
	targetImage->Update();

	// do the fitting
	typename TransformType::Pointer transform = TransformType::New();
	transform->SetStatisticalModel(model);
	transform->SetIdentity();

	// Setting up the fitting
	OptimizerType::Pointer optimizer = OptimizerType::New();
	optimizer->MinimizeOn();
	optimizer->SetMaximumNumberOfFunctionEvaluations(numberOfIterations);

	typedef  IterationStatusObserver ObserverType;
	ObserverType::Pointer observer = ObserverType::New();
	optimizer->AddObserver( itk::IterationEvent(), observer );

	typename TMetricType::Pointer metric = TMetricType::New();
	typename InterpolatorType::Pointer interpolator = InterpolatorType::New();

	typename RegistrationFilterType::Pointer registration = RegistrationFilterType::New();
	registration->SetInitialTransformParameters(transform->GetParameters());
	registration->SetMetric(metric);
	registration->SetOptimizer(   optimizer   );
	registration->SetTransform(   transform );
	registration->SetInterpolator( interpolator );
	registration->SetFixedImage( targetImage );
	registration->SetFixedImageRegion(targetImage->GetBufferedRegion() );
	registration->SetMovingImage( referenceImage );

	try {
		std::cout << "Performing registration... " << std::flush;
		registration->Update();
		std::cout << "[done]" << std::endl;

	} catch ( itk::ExceptionObject& o ) {
		std::cout << "caught exception " << o << std::endl;
	}

	typename TVectorImage::Pointer df = model->DrawSample(transform->GetCoefficients());

	// write deformation field
	if(outputDfFilename.size()>0){
		typename itk::ImageFileWriter<TVectorImage>::Pointer df_writer = itk::ImageFileWriter<TVectorImage>::New();
		df_writer->SetFileName(outputDfFilename);
		df_writer->SetInput(df);
		df_writer->Update();
	}

	
	// warp reference
	std::cout << "Warping reference... " << std::flush;
	typename WarperType::Pointer warper = WarperType::New();
	warper->SetInput(referenceImage  );
	warper->SetInterpolator( interpolator );
	warper->SetOutputSpacing( targetImage->GetSpacing() );
	warper->SetOutputOrigin( targetImage->GetOrigin() );
	warper->SetOutputDirection( targetImage->GetDirection() );
	warper->SetDisplacementField( df );
	warper->Update();
	std::cout << "[done]" << std::endl;

	return warper->GetOutput();
}
void QAngioSubstractionExtension::computeAutomateSingleImage()
{
    QApplication::setOverrideCursor(Qt::WaitCursor);
    const    unsigned int          Dimension = 2;
    typedef  Volume::ItkPixelType  PixelType;

    typedef itk::Image< PixelType, Dimension >  FixedImageType;
    typedef itk::Image< PixelType, Dimension >  MovingImageType;
    typedef   float     InternalPixelType;
    typedef itk::Image< InternalPixelType, Dimension > InternalImageType;

    typedef itk::TranslationTransform< double, Dimension > TransformType;
    typedef itk::GradientDescentOptimizer                  OptimizerType;
    typedef itk::LinearInterpolateImageFunction< 
                                    InternalImageType,
                                    double             > InterpolatorType;
    typedef itk::ImageRegistrationMethod< 
                                    InternalImageType, 
                                    InternalImageType >  RegistrationType;
    typedef itk::MutualInformationImageToImageMetric< 
                                          InternalImageType, 
                                          InternalImageType >    MetricType;

    TransformType::Pointer      transform     = TransformType::New();
    OptimizerType::Pointer      optimizer     = OptimizerType::New();
    InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
    RegistrationType::Pointer   registration  = RegistrationType::New();

    registration->SetOptimizer(optimizer);
    registration->SetTransform(transform);
    registration->SetInterpolator(interpolator);

    MetricType::Pointer         metric        = MetricType::New();
    registration->SetMetric(metric);
    metric->SetFixedImageStandardDeviation(0.4);
    metric->SetMovingImageStandardDeviation(0.4);
    metric->SetNumberOfSpatialSamples(50);

    typedef itk::ExtractImageFilter< Volume::ItkImageType, FixedImageType > FilterType;
    
    FilterType::Pointer extractFixedImageFilter = FilterType::New();
    Volume::ItkImageType::RegionType inputRegion = m_mainVolume->getItkData()->GetLargestPossibleRegion();
    Volume::ItkImageType::SizeType size = inputRegion.GetSize();
    //Dividim la mida per dos per tal de quedar-nos només amb la part central
    // ja que si no ens registre el background
    size[0] = size[0] / 2;
    size[1] = size[1] / 2;
    size[2] = 0;
    Volume::ItkImageType::IndexType start = inputRegion.GetIndex();
    const unsigned int sliceReference = m_imageSelectorSpinBox->value();
    //comencem a un quart de la imatge
    start[0] = size[0] / 2;
    start[1] = size[1] / 2;
    start[2] = sliceReference;
    Volume::ItkImageType::RegionType desiredRegion;
    desiredRegion.SetSize(size);
    desiredRegion.SetIndex(start);
    extractFixedImageFilter->SetExtractionRegion(desiredRegion);
    extractFixedImageFilter->SetInput(m_mainVolume->getItkData());
    extractFixedImageFilter->Update();

    FilterType::Pointer extractMovingImageFilter = FilterType::New();
    Volume::ItkImageType::IndexType startMoving = inputRegion.GetIndex();
    const unsigned int sliceNumber = m_2DView_1->getViewer()->getCurrentSlice();
    startMoving[0] = size[0] / 2;
    startMoving[1] = size[1] / 2;
    startMoving[2] = sliceNumber;
    Volume::ItkImageType::RegionType desiredMovingRegion;
    desiredMovingRegion.SetSize(size);
    desiredMovingRegion.SetIndex(startMoving);
    extractMovingImageFilter->SetExtractionRegion(desiredMovingRegion);
    extractMovingImageFilter->SetInput(m_mainVolume->getItkData());
    extractMovingImageFilter->Update();

    typedef itk::NormalizeImageFilter< 
                                FixedImageType, 
                                InternalImageType 
                                        > FixedNormalizeFilterType;

    typedef itk::NormalizeImageFilter< 
                                MovingImageType, 
                                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(extractFixedImageFilter->GetOutput());
    movingNormalizer->SetInput(extractMovingImageFilter->GetOutput());

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

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

    fixedNormalizer->Update();
    registration->SetFixedImageRegion(
       fixedNormalizer->GetOutput()->GetBufferedRegion());

    typedef RegistrationType::ParametersType ParametersType;
    ParametersType initialParameters(transform->GetNumberOfParameters());

    initialParameters[0] = 0.0;  // Initial offset in mm along X
    initialParameters[1] = 0.0;  // Initial offset in mm along Y

    registration->SetInitialTransformParameters(initialParameters);

    optimizer->SetLearningRate(20.0);
    optimizer->SetNumberOfIterations(200);
    optimizer->MaximizeOn();

    try 
    { 
        registration->Update();
    } 
    catch(itk::ExceptionObject & err) 
    { 
        std::cout << "ExceptionObject caught !" << std::endl; 
        std::cout << err << std::endl; 
        return;
    } 

    ParametersType finalParameters = registration->GetLastTransformParameters();

    double TranslationAlongX = finalParameters[0];
    double TranslationAlongY = finalParameters[1];

    // Print out results
    //
    DEBUG_LOG(QString("Result = "));
    DEBUG_LOG(QString(" Translation X = %1").arg(TranslationAlongX));
    DEBUG_LOG(QString(" Translation Y = %1").arg(TranslationAlongY));
    DEBUG_LOG(QString(" Iterations    = %1").arg(optimizer->GetCurrentIteration()));
    DEBUG_LOG(QString(" Metric value  = %1").arg(optimizer->GetValue()));
    double spacing[3];
    m_mainVolume->getSpacing(spacing);
    DEBUG_LOG(QString(" Translation X (in px) = %1").arg(TranslationAlongX / spacing[0]));
    DEBUG_LOG(QString(" Translation Y (in px) = %1").arg(TranslationAlongY / spacing[1]));

    //Actualitzem les dades de la transdifference tool
    m_toolManager->triggerTool("TransDifferenceTool");
    TransDifferenceTool* tdTool = static_cast<TransDifferenceTool*> (m_2DView_2->getViewer()->getToolProxy()->getTool("TransDifferenceTool"));
    if(m_tdToolData == 0){
        m_tdToolData = static_cast<TransDifferenceToolData*> (tdTool->getToolData());
    }
    if(m_tdToolData->getInputVolume() != m_mainVolume){
        m_tdToolData->setInputVolume(m_mainVolume);
    }
    tdTool->setSingleDifferenceImage(TranslationAlongX / spacing[0],TranslationAlongY / spacing[1]);
    m_toolManager->triggerTool("SlicingTool");
    

/*    typedef itk::Image< PixelType, Dimension >  FixedImageType;
    typedef itk::Image< PixelType, Dimension >  MovingImageType;
    typedef itk::TranslationTransform< double, Dimension > TransformType;
    typedef itk::RegularStepGradientDescentOptimizer       OptimizerType;
    typedef itk::MattesMutualInformationImageToImageMetric< 
                                          FixedImageType, 
                                          MovingImageType >    MetricType;
    typedef itk:: LinearInterpolateImageFunction< 
                                    MovingImageType,
                                    double          >    InterpolatorType;
    typedef itk::ImageRegistrationMethod< 
                                    FixedImageType, 
                                    MovingImageType >    RegistrationType;

    MetricType::Pointer         metric        = MetricType::New();
    TransformType::Pointer      transform     = TransformType::New();
    OptimizerType::Pointer      optimizer     = OptimizerType::New();
    InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
    RegistrationType::Pointer   registration  = RegistrationType::New();

    registration->SetMetric(metric);
    registration->SetOptimizer(optimizer);
    registration->SetTransform(transform);
    registration->SetInterpolator(interpolator);

    metric->SetNumberOfHistogramBins(50);
    metric->SetNumberOfSpatialSamples(10000);

    typedef itk::ExtractImageFilter< Volume::ItkImageType, FixedImageType > FilterType;
    
    FilterType::Pointer extractFixedImageFilter = FilterType::New();
    Volume::ItkImageType::RegionType inputRegion = m_mainVolume->getItkData()->GetLargestPossibleRegion();
    Volume::ItkImageType::SizeType size = inputRegion.GetSize();
    //Dividim la mida per dos per tal de quedar-nos només amb la part central
    // ja que si no ens registre el background
    size[0] = size[0] / 2;
    size[1] = size[1] / 2;
    size[2] = 0;
    Volume::ItkImageType::IndexType start = inputRegion.GetIndex();
    const unsigned int sliceReference = m_imageSelectorSpinBox->value();
    //comencem a un quart de la imatge
    start[0] = size[0] / 2;
    start[1] = size[1] / 2;
    start[2] = sliceReference;
    Volume::ItkImageType::RegionType desiredRegion;
    desiredRegion.SetSize(size);
    desiredRegion.SetIndex(start);
    extractFixedImageFilter->SetExtractionRegion(desiredRegion);
    extractFixedImageFilter->SetInput(m_mainVolume->getItkData());
    extractFixedImageFilter->Update();

    FilterType::Pointer extractMovingImageFilter = FilterType::New();
    Volume::ItkImageType::IndexType startMoving = inputRegion.GetIndex();
    const unsigned int sliceNumber = m_2DView_1->getViewer()->getCurrentSlice();
    startMoving[0] = size[0] / 2;
    startMoving[1] = size[1] / 2;
    startMoving[2] = sliceNumber;
    Volume::ItkImageType::RegionType desiredMovingRegion;
    desiredMovingRegion.SetSize(size);
    desiredMovingRegion.SetIndex(startMoving);
    extractMovingImageFilter->SetExtractionRegion(desiredMovingRegion);
    extractMovingImageFilter->SetInput(m_mainVolume->getItkData());
    extractMovingImageFilter->Update();

    registration->SetFixedImage(extractFixedImageFilter->GetOutput());
    registration->SetMovingImage(extractMovingImageFilter->GetOutput());

    typedef RegistrationType::ParametersType ParametersType;
    ParametersType initialParameters(transform->GetNumberOfParameters());

    //Potser seria millor posar la transformada que té actualment
    initialParameters[0] = 0.0;  // Initial offset in mm along X
    initialParameters[1] = 0.0;  // Initial offset in mm along Y

    registration->SetInitialTransformParameters(initialParameters);

    optimizer->SetMaximumStepLength(4.00);  
    optimizer->SetMinimumStepLength(0.005);

    optimizer->SetNumberOfIterations(200);

    try 
    { 
        registration->StartRegistration(); 
    } 
    catch(itk::ExceptionObject & err) 
    { 
        DEBUG_LOG(QString("ExceptionObject caught !"));
        std::cout<<err<<std::endl;
        return;
    } 
    ParametersType finalParameters = registration->GetLastTransformParameters();

    const double TranslationAlongX = finalParameters[0];
    const double TranslationAlongY = finalParameters[1];

    const unsigned int numberOfIterations = optimizer->GetCurrentIteration();

    const double bestValue = optimizer->GetValue();

    DEBUG_LOG(QString("Result = "));
    DEBUG_LOG(QString(" Translation X = %1").arg(TranslationAlongX));
    DEBUG_LOG(QString(" Translation Y = %1").arg(TranslationAlongY));
    DEBUG_LOG(QString(" Iterations    = %1").arg(numberOfIterations));
    DEBUG_LOG(QString(" Metric value  = %1").arg(bestValue));

    typedef  unsigned char  OutputPixelType;
    typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
    typedef itk::RescaleIntensityImageFilter< FixedImageType, FixedImageType > RescaleFilterType;
    typedef itk::ResampleImageFilter< 
                            FixedImageType, 
                            FixedImageType >    ResampleFilterType;
    typedef itk::CastImageFilter< 
                        FixedImageType,
                        OutputImageType > CastFilterType;
    typedef itk::ImageFileWriter< OutputImageType >  WriterType;

    WriterType::Pointer      writer =  WriterType::New();
    CastFilterType::Pointer  caster =  CastFilterType::New();
    ResampleFilterType::Pointer resample = ResampleFilterType::New();
    RescaleFilterType::Pointer rescaler = RescaleFilterType::New();

    rescaler->SetOutputMinimum(0);
    rescaler->SetOutputMaximum(255);

    TransformType::Pointer finalTransform = TransformType::New();
    finalTransform->SetParameters(finalParameters);
    resample->SetTransform(finalTransform);
    resample->SetSize(extractMovingImageFilter->GetOutput()->GetLargestPossibleRegion().GetSize());
    resample->SetOutputOrigin(extractMovingImageFilter->GetOutput()->GetOrigin());
    resample->SetOutputSpacing(extractMovingImageFilter->GetOutput()->GetSpacing());
    resample->SetDefaultPixelValue(100);

    writer->SetFileName("prova.jpg");

    rescaler->SetInput(extractMovingImageFilter->GetOutput());
    resample->SetInput(rescaler->GetOutput());
    caster->SetInput(resample->GetOutput());
    writer->SetInput(caster->GetOutput());
    writer->Update();
*/

    QApplication::restoreOverrideCursor();

}