int main( int argc, char *argv[] )
{
if( argc < 4 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile ";
std::cerr << " outputImagefile [differenceBeforeRegistration] ";
std::cerr << " [differenceAfterRegistration] ";
std::cerr << " [sliceBeforeRegistration] ";
std::cerr << " [sliceDifferenceBeforeRegistration] ";
std::cerr << " [sliceDifferenceAfterRegistration] ";
std::cerr << " [sliceAfterRegistration] " << std::endl;
return EXIT_FAILURE;
}
const unsigned int Dimension = 3;
typedef float PixelType;
typedef itk::Image< PixelType, Dimension > FixedImageType;
typedef itk::Image< PixelType, Dimension > MovingImageType;
// Software Guide : BeginLatex
//
// The Transform class is instantiated using the code below. The only
// template parameter to this class is the representation type of the
// space coordinates.
//
// \index{itk::Versor\-Rigid3D\-Transform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet

// Software Guide : EndCodeSnippet


typedef itk:: LinearInterpolateImageFunction< MovingImageType, double > InterpolatorType;
typedef itk::ImageRegistrationMethod< FixedImageType, MovingImageType > RegistrationType;

MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetInterpolator( interpolator );
// Software Guide : BeginLatex
//
// The transform object is constructed below and passed to the registration
// method.
//
// \index{itk::Versor\-Rigid3D\-Transform!New()}
// \index{itk::Versor\-Rigid3D\-Transform!Pointer}
// \index{itk::Registration\-Method!SetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
registration->SetTransform( transform );
// Software Guide : EndCodeSnippet
typedef itk::ImageFileReader< FixedImageType > FixedImageReaderType;
typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;
FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();
fixedImageReader->SetFileName( argv[1] );
movingImageReader->SetFileName( argv[2] );
registration->SetFixedImage( fixedImageReader->GetOutput() );
registration->SetMovingImage( movingImageReader->GetOutput() );
fixedImageReader->Update();
registration->SetFixedImageRegion(
fixedImageReader->GetOutput()->GetBufferedRegion() );
// Software Guide : BeginLatex
//
// The input images are taken from readers. It is not necessary here to
// explicitly call \code{Update()} on the readers since the
// \doxygen{CenteredTransformInitializer} will do it as part of its
// computations. The following code instantiates the type of the
// initializer. This class is templated over the fixed and moving image type
// as well as the transform type. An initializer is then constructed by
// calling the \code{New()} method and assigning the result to a smart
// pointer.
//
// \index{itk::Centered\-Transform\-Initializer!Instantiation}
// \index{itk::Centered\-Transform\-Initializer!New()}
// \index{itk::Centered\-Transform\-Initializer!SmartPointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : BeginLatex
//
// Let's execute this example over some of the images available in the ftp
// site
//
// \url{ftp://public.kitware.com/pub/itk/Data/BrainWeb}
//
// Note that the images in the ftp site are compressed in \code{.tgz} files.
// You should download these files an uncompress them in your local system.
// After decompressing and extracting the files you could take a pair of
// volumes, for example the pair:
//
// \begin{itemize}
// \item \code{brainweb1e1a10f20.mha}
// \item \code{brainweb1e1a10f20Rot10Tx15.mha}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first image
// by $10$ degrees around the origin and shifting it $15mm$ in $X$. The
// registration takes $24$ iterations and produces:
//
// \begin{center}
// \begin{verbatim}
// [-6.03744e-05, 5.91487e-06, -0.0871932, 2.64659, -17.4637, -0.00232496]
// \end{verbatim}
// \end{center}
//
// That are interpreted as
//
// \begin{itemize}
// \item Versor = $(-6.03744e-05, 5.91487e-06, -0.0871932)$
// \item Translation = $(2.64659, -17.4637, -0.00232496)$ millimeters
// \end{itemize}
//
// This Versor is equivalent to a rotation of $9.98$ degrees around the $Z$
// axis.
//
// Note that the reported translation is not the translation of $(15.0,0.0,0.0)$
// that we may be naively expecting. The reason is that the
// \code{VersorRigid3DTransform} is applying the rotation around the center
// found by the \code{CenteredTransformInitializer} and then adding the
// translation vector shown above.
//
// It is more illustrative in this case to take a look at the actual
// rotation matrix and offset resulting form the $6$ parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
transform->SetParameters( finalParameters );
TransformType::MatrixType matrix = transform->GetMatrix();
TransformType::OffsetType offset = transform->GetOffset();
std::cout << "Matrix = " << std::endl << matrix << std::endl;
std::cout << "Offset = " << std::endl << offset << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output of this print statements is
//
// \begin{center}
// \begin{verbatim}
// Matrix =
// 0.984795 0.173722 2.23132e-05
// -0.173722 0.984795 0.000119257
// -1.25621e-06 -0.00012132 1
//
// Offset =
// [-15.0105, -0.00672343, 0.0110854]
// \end{verbatim}
// \end{center}
//
// From the rotation matrix it is possible to deduce that the rotation is
// happening in the X,Y plane and that the angle is on the order of
// $\arcsin{(0.173722)}$ which is very close to 10 degrees, as we expected.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17}
// \itkcaption[CenteredTransformInitializer input images]{Fixed and moving image
// provided as input to the registration method using
// CenteredTransformInitializer.}
// \label{fig:FixedMovingImageRegistration8}
// \end{figure}
//
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration8Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration8DifferenceBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration8DifferenceAfter}
// \itkcaption[CenteredTransformInitializer output images]{Resampled moving
// image (left). Differences between fixed and moving images, before (center)
// and after (right) registration with the
// CenteredTransformInitializer.}
// \label{fig:ImageRegistration8Outputs}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration8Outputs} shows the output of the
// registration. The center image in this figure shows the differences
// between the fixed image and the resampled moving image before the
// registration. The image on the right side presents the difference between
// the fixed image and the resampled moving image after the registration has
// been performed. Note that these images are individual slices extracted
// from the actual volumes. For details, look at the source code of this
// example, where the ExtractImageFilter is used to extract a slice from the
// the center of each one of the volumes. One of the main purposes of this
// example is to illustrate that the toolkit can perform registration on
// images of any dimension. The only limitations are, as usual, the amount of
// memory available for the images and the amount of computation time that it
// will take to complete the optimization process.
//
// \begin{figure}
// \center
// \includegraphics[height=0.32\textwidth]{ImageRegistration8TraceMetric}
// \includegraphics[height=0.32\textwidth]{ImageRegistration8TraceAngle}
// \includegraphics[height=0.32\textwidth]{ImageRegistration8TraceTranslations}
// \itkcaption[CenteredTransformInitializer output plots]{Plots of the metric,
// rotation angle, center of rotation and translations during the
// registration using CenteredTransformInitializer.}
// \label{fig:ImageRegistration8Plots}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration8Plots} shows the plots of the main
// output parameters of the registration process. The metric values at every
// iteration. The Z component of the versor is plotted as an indication of
// how the rotation progress. The X,Y translation components of the
// registration are plotted at every iteration too.
//
// Shell and Gnuplot scripts for generating the diagrams in
// Figure~\ref{fig:ImageRegistration8Plots} are available in the directory
//
// \code{InsightDocuments/SoftwareGuide/Art}
//
// You are strongly encouraged to run the example code, since only in this
// way you can gain a first hand experience with the behavior of the
// registration process. Once again, this is a simple reflection of the
// philosophy that we put forward in this book:
//
// \emph{If you can not replicate it, then it does not exist!}.
//
// We have seen enough published papers with pretty pictures, presenting
// results that in practice are impossible to replicate. That is vanity, not
// science.
//
// Software Guide : EndLatex
typedef itk::ResampleImageFilter<
MovingImageType,
FixedImageType > ResampleFilterType;
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetCenter( transform->GetCenter() );
finalTransform->SetParameters( finalParameters );
finalTransform->SetFixedParameters( transform->GetFixedParameters() );
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetTransform( finalTransform );
resampler->SetInput( movingImageReader->GetOutput() );
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
resampler->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resampler->SetOutputOrigin( fixedImage->GetOrigin() );
resampler->SetOutputSpacing( fixedImage->GetSpacing() );
resampler->SetOutputDirection( fixedImage->GetDirection() );
resampler->SetDefaultPixelValue( 100 );
typedef unsigned char OutputPixelType;
typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
typedef itk::CastImageFilter< FixedImageType, OutputImageType > CastFilterType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName( argv[3] );
caster->SetInput( resampler->GetOutput() );
writer->SetInput( caster->GetOutput() );
writer->Update();
typedef itk::SubtractImageFilter<
FixedImageType,
FixedImageType,
FixedImageType > DifferenceFilterType;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
typedef itk::RescaleIntensityImageFilter<
FixedImageType,
OutputImageType > RescalerType;
RescalerType::Pointer intensityRescaler = RescalerType::New();
intensityRescaler->SetInput( difference->GetOutput() );
intensityRescaler->SetOutputMinimum( 0 );
intensityRescaler->SetOutputMaximum( 255 );
difference->SetInput1( fixedImageReader->GetOutput() );
difference->SetInput2( resampler->GetOutput() );
resampler->SetDefaultPixelValue( 1 );
WriterType::Pointer writer2 = WriterType::New();
writer2->SetInput( intensityRescaler->GetOutput() );
// Compute the difference image between the
// fixed and resampled moving image.
if( argc > 5 )
{
writer2->SetFileName( argv[5] );
writer2->Update();
}
typedef itk::IdentityTransform< double, Dimension > IdentityTransformType;
IdentityTransformType::Pointer identity = IdentityTransformType::New();
// Compute the difference image between the
// fixed and moving image before registration.
if( argc > 4 )
{
resampler->SetTransform( identity );
writer2->SetFileName( argv[4] );
writer2->Update();
}
//
// Here we extract slices from the input volume, and the difference volumes
// produced before and after the registration. These slices are presented as
// figures in the Software Guide.
//
//
typedef itk::Image< OutputPixelType, 2 > OutputSliceType;
typedef itk::ExtractImageFilter<
OutputImageType,
OutputSliceType > ExtractFilterType;
ExtractFilterType::Pointer extractor = ExtractFilterType::New();
extractor->SetDirectionCollapseToSubmatrix();
extractor->InPlaceOn();
FixedImageType::RegionType inputRegion =
fixedImage->GetLargestPossibleRegion();
FixedImageType::SizeType size = inputRegion.GetSize();
FixedImageType::IndexType start = inputRegion.GetIndex();
// Select one slice as output
size[2] = 0;
start[2] = 90;
FixedImageType::RegionType desiredRegion;
desiredRegion.SetSize( size );
desiredRegion.SetIndex( start );
extractor->SetExtractionRegion( desiredRegion );
typedef itk::ImageFileWriter< OutputSliceType > SliceWriterType;
SliceWriterType::Pointer sliceWriter = SliceWriterType::New();
sliceWriter->SetInput( extractor->GetOutput() );
if( argc > 6 )
{
extractor->SetInput( caster->GetOutput() );
resampler->SetTransform( identity );
sliceWriter->SetFileName( argv[6] );
sliceWriter->Update();
}
if( argc > 7 )
{
extractor->SetInput( intensityRescaler->GetOutput() );
resampler->SetTransform( identity );
sliceWriter->SetFileName( argv[7] );
sliceWriter->Update();
}
if( argc > 8 )
{
resampler->SetTransform( finalTransform );
sliceWriter->SetFileName( argv[8] );
sliceWriter->Update();
}
if( argc > 9 )
{
extractor->SetInput( caster->GetOutput() );
resampler->SetTransform( finalTransform );
sliceWriter->SetFileName( argv[9] );
sliceWriter->Update();
}
return EXIT_SUCCESS;
}
Пример #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());
}
bool mitk::NavigationDataLandmarkTransformFilter::FindCorrespondentLandmarks(LandmarkPointContainer& sources, const LandmarkPointContainer& targets) const
{
  if (sources.size() < 6 || targets.size() < 6)
    return false;
  //throw std::invalid_argument("ICP correspondence finding needs at least 6 landmarks");

  /* lots of type definitions */
  typedef itk::PointSet<mitk::ScalarType, 3> PointSetType;
  //typedef itk::BoundingBox<PointSetType::PointIdentifier, PointSetType::PointDimension> BoundingBoxType;

  typedef itk::EuclideanDistancePointMetric< PointSetType, PointSetType> MetricType;
  //typedef MetricType::TransformType TransformBaseType;
  //typedef MetricType::TransformType::ParametersType ParametersType;
  //typedef TransformBaseType::JacobianType JacobianType;
  //typedef itk::Euler3DTransform< double > TransformType;
  typedef itk::VersorRigid3DTransform< double > TransformType;
  typedef TransformType ParametersType;
  typedef itk::PointSetToPointSetRegistrationMethod< PointSetType, PointSetType > RegistrationType;

  /* copy landmarks to itk pointsets for registration */
  PointSetType::Pointer sourcePointSet = PointSetType::New();
  unsigned int i = 0;
  for (LandmarkPointContainer::const_iterator it = sources.begin(); it != sources.end(); ++it)
  {
    PointSetType::PointType doublePoint;
    mitk::itk2vtk(*it, doublePoint); // copy mitk::ScalarType point into double point as workaround to ITK 3.10 bug
    sourcePointSet->SetPoint(i++, doublePoint /**it*/);
  }

  i = 0;
  PointSetType::Pointer targetPointSet = PointSetType::New();
  for (LandmarkPointContainer::const_iterator it = targets.begin(); it != targets.end(); ++it)
  {
    PointSetType::PointType doublePoint;
    mitk::itk2vtk(*it, doublePoint); // copy mitk::ScalarType point into double point as workaround to ITK 3.10 bug
    targetPointSet->SetPoint(i++, doublePoint /**it*/);
  }

  /* get centroid and extends of our pointsets */
  //BoundingBoxType::Pointer sourceBoundingBox = BoundingBoxType::New();
  //sourceBoundingBox->SetPoints(sourcePointSet->GetPoints());
  //sourceBoundingBox->ComputeBoundingBox();
  //BoundingBoxType::Pointer targetBoundingBox = BoundingBoxType::New();
  //targetBoundingBox->SetPoints(targetPointSet->GetPoints());
  //targetBoundingBox->ComputeBoundingBox();


  TransformType::Pointer transform = TransformType::New();
  transform->SetIdentity();
  //transform->SetTranslation(targetBoundingBox->GetCenter() - sourceBoundingBox->GetCenter());

  itk::LevenbergMarquardtOptimizer::Pointer optimizer = itk::LevenbergMarquardtOptimizer::New();
  optimizer->SetUseCostFunctionGradient(false);

  RegistrationType::Pointer registration = RegistrationType::New();

  // Scale the translation components of the Transform in the Optimizer
  itk::LevenbergMarquardtOptimizer::ScalesType scales(transform->GetNumberOfParameters());
  const double translationScale = 5000; //sqrtf(targetBoundingBox->GetDiagonalLength2())  * 1000; // dynamic range of translations
  const double rotationScale = 1.0; // dynamic range of rotations
  scales[0] = 1.0 / rotationScale;
  scales[1] = 1.0 / rotationScale;
  scales[2] = 1.0 / rotationScale;
  scales[3] = 1.0 / translationScale;
  scales[4] = 1.0 / translationScale;
  scales[5] = 1.0 / translationScale;
  //scales.Fill(0.01);
  unsigned long numberOfIterations = 80000;
  double gradientTolerance = 1e-10; // convergence criterion
  double valueTolerance = 1e-10; // convergence criterion
  double epsilonFunction = 1e-10; // convergence criterion
  optimizer->SetScales( scales );
  optimizer->SetNumberOfIterations( numberOfIterations );
  optimizer->SetValueTolerance( valueTolerance );
  optimizer->SetGradientTolerance( gradientTolerance );
  optimizer->SetEpsilonFunction( epsilonFunction );


  registration->SetInitialTransformParameters( transform->GetParameters() );
  //------------------------------------------------------
  // Connect all the components required for Registration
  //------------------------------------------------------
  MetricType::Pointer metric = MetricType::New();

  registration->SetMetric( metric );
  registration->SetOptimizer( optimizer );
  registration->SetTransform( transform );
  registration->SetFixedPointSet( targetPointSet );
  registration->SetMovingPointSet( sourcePointSet );

  try
  {
    //registration->StartRegistration();
    registration->Update();
  }
  catch( itk::ExceptionObject & e )
  {
    MITK_INFO << "Exception caught during ICP optimization: " << e;
    return false;
    //throw e;
  }
  MITK_INFO << "ICP successful: Solution = " << transform->GetParameters() << std::endl;
  MITK_INFO << "Metric value: " << metric->GetValue(transform->GetParameters());

  /* find point correspondences */
  //mitk::PointLocator::Pointer pointLocator = mitk::PointLocator::New();  // <<- use mitk::PointLocator instead of searching manually?
  //pointLocator->SetPoints()
  for (LandmarkPointContainer::const_iterator sourcesIt = sources.begin(); sourcesIt != sources.end(); ++sourcesIt)
  {
  }
  //MetricType::MeasureType closestDistances = metric->GetValue(transform->GetParameters());
  //unsigned int index = 0;
  LandmarkPointContainer sortedSources;
  for (LandmarkPointContainer::const_iterator targetsIt = targets.begin(); targetsIt != targets.end(); ++targetsIt)
  {
    double minDistance = itk::NumericTraits<double>::max();
    LandmarkPointContainer::iterator minDistanceIterator = sources.end();
    for (LandmarkPointContainer::iterator sourcesIt = sources.begin(); sourcesIt != sources.end(); ++sourcesIt)
    {
      TransformInitializerType::LandmarkPointType transformedSource = transform->TransformPoint(*sourcesIt);
      double dist = targetsIt->EuclideanDistanceTo(transformedSource);
      MITK_INFO << "target: " << *targetsIt << ", source: " << *sourcesIt << ", transformed source: " << transformedSource << ", dist: " << dist;
      if (dist < minDistance )
      {
        minDistanceIterator = sourcesIt;
        minDistance = dist;
      }
    }
    if (minDistanceIterator == sources.end())
      return false;
    MITK_INFO << "minimum distance point is: " << *minDistanceIterator << " (dist: " << targetsIt->EuclideanDistanceTo(transform->TransformPoint(*minDistanceIterator)) << ", minDist: " << minDistance << ")";
    sortedSources.push_back(*minDistanceIterator); // this point is assigned
    sources.erase(minDistanceIterator); // erase it from sources to avoid duplicate assigns
  }
  //for (LandmarkPointContainer::const_iterator sortedSourcesIt = sortedSources.begin(); targetsIt != sortedSources.end(); ++targetsIt)
  sources = sortedSources;
  return true;
}
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);
}
Пример #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();
    }
Пример #6
0
void MRFRegistrationDisplay::startRegistration()
{
	////////////////////////////////////////////////////////////////
	// We move through the list of moving images registering them
	// to the fixed image
	////////////////////////////////////////////////////////////////
	ImageType::Pointer workingImage = itkMovingImages.front()->GetOutput();


	int numImages = this->itkMovingImages.size();
	for(int imageNum = 0; imageNum < numImages; imageNum++)
	{
		typedef itk::BSplineInterpolateImageFunction<ImageType, double> InterpolatorType;

		GridImageType::Pointer gridImage = GridImageType::New();
		createGridImage(gridImage);

		// create a new registration object
		RegistrationType::Pointer registration = RegistrationType::New();
		InterpolatorType::Pointer interpolator = InterpolatorType::New();

		// set the registration parameters
		registration->SetFixedImage(this->itkFixedImage);
		registration->SetMovingImage(workingImage);
		registration->SetInterpolator(interpolator);
		registration->SetBSplineGrid(gridImage);
		registration->SetImageLevels(this->resolutionLevels);
		registration->SetLabelSteps(this->labelSteps);
		registration->SetLabelScaleFactor(this->labelInc);
		registration->SetSparseSampling(this->useSparseSampling);
		registration->SetMaximumDisplacement(this->maxDisplacement);
		registration->SetLinkMaximumDisplacementToGrid(false);
		registration->SetOptimiserIterations(this->optimiserIterations);
		registration->SetRegularisationLambda(this->regularisationAmount);
		registration->SetPotentialType(RegistrationType::NCC);


		try {
			registration->Update();
		}
		catch(itk::ExceptionObject &e)
		{
			std::cout << "F**k" << std::endl;
			std::cout << e << std::endl;
			return;
		}



	}


	// create the grid image
	GridImageType::Pointer gridImage = GridImageType::New();
	this->createGridImage(gridImage);




}
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();

}