static mitk::Image::Pointer ResampleDWIbySpacing(mitk::Image::Pointer input, float* spacing)
{
itk::Vector<double, 3> spacingVector;
spacingVector[0] = spacing[0];
spacingVector[1] = spacing[1];
spacingVector[2] = spacing[2];
typedef itk::ResampleDwiImageFilter<short> ResampleFilterType;
mitk::DiffusionPropertyHelper::ImageType::Pointer itkVectorImagePointer = mitk::DiffusionPropertyHelper::ImageType::New();
mitk::CastToItkImage(input, itkVectorImagePointer);
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetInput( itkVectorImagePointer );
resampler->SetInterpolation(ResampleFilterType::Interpolate_Linear);
resampler->SetNewSpacing(spacingVector);
resampler->Update();
mitk::Image::Pointer output = mitk::GrabItkImageMemory( resampler->GetOutput() );
output->GetPropertyList()->ReplaceProperty( mitk::DiffusionPropertyHelper::GRADIENTCONTAINERPROPERTYNAME.c_str(), mitk::GradientDirectionsProperty::New( mitk::DiffusionPropertyHelper::GetGradientContainer(input) ) );
output->GetPropertyList()->ReplaceProperty( mitk::DiffusionPropertyHelper::REFERENCEBVALUEPROPERTYNAME.c_str(), mitk::FloatProperty::New( mitk::DiffusionPropertyHelper::GetReferenceBValue(input) ) );
mitk::DiffusionPropertyHelper propertyHelper( output );
propertyHelper.InitializeImage();
return output;
}
static mitk::Image::Pointer TransformToReference(mitk::Image *reference, mitk::Image *moving, bool sincInterpol = false)
{
// Convert to itk Images
InputImageType::Pointer itkReference = InputImageType::New();
InputImageType::Pointer itkMoving = InputImageType::New();
mitk::CastToItkImage(reference,itkReference);
mitk::CastToItkImage(moving,itkMoving);
// Identify Transform
typedef itk::IdentityTransform<double, 3> T_Transform;
T_Transform::Pointer _pTransform = T_Transform::New();
_pTransform->SetIdentity();
typedef itk::WindowedSincInterpolateImageFunction< InputImageType, 3> WindowedSincInterpolatorType;
WindowedSincInterpolatorType::Pointer sinc_interpolator = WindowedSincInterpolatorType::New();
typedef itk::ResampleImageFilter<InputImageType, InputImageType> ResampleFilterType;
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetInput(itkMoving);
resampler->SetReferenceImage( itkReference );
resampler->UseReferenceImageOn();
resampler->SetTransform(_pTransform);
resampler->SetInterpolator(sinc_interpolator);
resampler->Update();
// Convert back to mitk
mitk::Image::Pointer result = mitk::Image::New();
result->InitializeByItk(resampler->GetOutput());
GrabItkImageMemory( resampler->GetOutput() , result );
return result;
}
// 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());
}
FilterImageType::Pointer gaussTransform(const FilterImageType::Pointer sourceImage, FilterImageType::SizeType destSize,
float gaussSigma, const BaseTransformType *transform) {
std::cerr << "Filter, Transformation and Output start" << std::endl;
itk::TimeProbesCollectorBase collector;
collector.Start( "Filter, Transformation and Output" );
collector.Start( "Filter" );
std::cerr << "dbg:" << __FILE__ << " line:" << __LINE__ << std::endl;
FilterImageType::SizeType sourceSize = sourceImage->GetBufferedRegion().GetSize();
std::cerr << "dbg:" << __FILE__ << " line:" << __LINE__ << std::endl;
FilterImageType::ConstPointer temp;
#ifdef useFastMemoryHungryGaussian
{
GaussFilterType::Pointer filter = GaussFilterType::New();
filter->SetDirection( 0 ); // 0 --> X direction
filter->SetOrder( GaussFilterType::ZeroOrder );
filter->SetSigma( gaussSigma );
filter->SetInput( sourceImage );
std::cerr << "dbg:" << __FILE__ << " line:" << __LINE__ << std::endl;
try { filter->Update(); }
catch( itk::ExceptionObject & excep ) { std::cerr << "Exception caught !" << std::endl << excep << std::endl; }
temp = filter->GetOutput();
}
{
GaussFilterType::Pointer filter = GaussFilterType::New();
filter->SetDirection( 1 ); // 1 --> Y direction
filter->SetOrder( GaussFilterType::ZeroOrder );
filter->SetSigma( gaussSigma );
filter->SetInput( temp );
std::cerr << "dbg:" << __FILE__ << " line:" << __LINE__ << std::endl;
try { filter->Update(); }
catch( itk::ExceptionObject & excep ) { std::cerr << "Exception caught !" << std::endl << excep << std::endl; }
temp = filter->GetOutput();
}
{
GaussFilterType::Pointer filter = GaussFilterType::New();
filter->SetDirection( 2 ); // 2 --> Z direction
filter->SetOrder( GaussFilterType::ZeroOrder );
filter->SetSigma( gaussSigma );
filter->SetInput( temp );
std::cerr << "dbg:" << __FILE__ << " line:" << __LINE__ << std::endl;
try { filter->Update(); }
catch( itk::ExceptionObject & excep ) { std::cerr << "Exception caught !" << std::endl << excep << std::endl; }
temp = filter->GetOutput();
}
#else
{
GaussFilterType::Pointer filter = GaussFilterType::New();
filter->SetUseImageSpacingOn();
filter->SetVariance( std::sqrt(gaussSigma) );
filter->SetInput( sourceImage );
std::cerr << "dbg:" << __FILE__ << " line:" << __LINE__ << std::endl;
try { filter->Update(); }
catch( itk::ExceptionObject & excep ) { std::cerr << "Exception caught !" << std::endl << excep << std::endl; }
temp = filter->GetOutput();
}
#endif
collector.Stop( "Filter" );
InterpolatorType::Pointer interpolator = InterpolatorType::New();
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetInterpolator( interpolator );
IdentityTransformType::Pointer identPointer;
if (transform == NULL) {
identPointer = IdentityTransformType::New();
transform = identPointer;
}
resampler->SetTransform( transform );
FilterImageType::SpacingType destSpacing;
for(unsigned int i = 0; i < Dimension; ++i) {
destSpacing[i] = sourceImage->GetSpacing()[i] * float(sourceSize[i]) / float(destSize[i]);
}
resampler->SetInput( temp );
std::cerr << "dbg:" << __FILE__ << " line:" << __LINE__ << std::endl;
resampler->SetSize( destSize );
resampler->SetOutputSpacing( destSpacing );
resampler->SetOutputOrigin( sourceImage->GetOrigin() );
resampler->SetDefaultPixelValue( 0 );
std::cerr << "dbg:" << __FILE__ << " line:" << __LINE__ << std::endl;
FilterImageType::Pointer result;
try { resampler->Update(); result = resampler->GetOutput(); }
catch( itk::ExceptionObject & excep ) { std::cerr << "Exception caught !" << std::endl << excep << std::endl; }
std::cerr << "dbg:" << __FILE__ << " line:" << __LINE__ << std::endl;
collector.Stop( "Filter, Transformation and Output" );
collector.Report();
std::cerr << "Filter, Transformation and Output end" << std::endl;
return result;
}
void mitk::RegistrationWrapper::ApplyTransformationToImage(mitk::Image::Pointer img, const mitk::RegistrationWrapper::RidgidTransformType &transformation,double* offset, mitk::Image* resampleReference, bool binary)
{
typedef mitk::DiffusionImage<short> DiffusionImageType;
if (dynamic_cast<DiffusionImageType*> (img.GetPointer()) == NULL)
{
ItkImageType::Pointer itkImage = ItkImageType::New();
MITK_ERROR << "imgCopy 0 " << "/" << img->GetReferenceCount();
MITK_ERROR << "pixel type " << img->GetPixelType().GetComponentTypeAsString();
CastToItkImage(img, itkImage);
typedef itk::Euler3DTransform< double > RigidTransformType;
RigidTransformType::Pointer rtransform = RigidTransformType::New();
RigidTransformType::ParametersType parameters(RigidTransformType::ParametersDimension);
for (int i = 0; i<6;++i)
parameters[i] = transformation[i];
rtransform->SetParameters( parameters );
mitk::Point3D origin = itkImage->GetOrigin();
origin[0]-=offset[0];
origin[1]-=offset[1];
origin[2]-=offset[2];
mitk::Point3D newOrigin = rtransform->GetInverseTransform()->TransformPoint(origin);
itk::Matrix<double,3,3> dir = itkImage->GetDirection();
itk::Matrix<double,3,3> transM ( vnl_inverse(rtransform->GetMatrix().GetVnlMatrix()));
itk::Matrix<double,3,3> newDirection = transM * dir;
itkImage->SetOrigin(newOrigin);
itkImage->SetDirection(newDirection);
// Perform Resampling if reference image is provided
if (resampleReference != NULL)
{
typedef itk::ResampleImageFilter<ItkImageType, ItkImageType> ResampleFilterType;
ItkImageType::Pointer itkReference = ItkImageType::New();
CastToItkImage(resampleReference,itkReference);
typedef itk::WindowedSincInterpolateImageFunction< ItkImageType, 3> WindowedSincInterpolatorType;
WindowedSincInterpolatorType::Pointer sinc_interpolator = WindowedSincInterpolatorType::New();
typedef itk::NearestNeighborInterpolateImageFunction< ItkImageType, double > NearestNeighborInterpolatorType;
NearestNeighborInterpolatorType::Pointer nn_interpolator = NearestNeighborInterpolatorType::New();
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetInput(itkImage);
resampler->SetReferenceImage( itkReference );
resampler->UseReferenceImageOn();
if (binary)
resampler->SetInterpolator(nn_interpolator);
else
resampler->SetInterpolator(sinc_interpolator);
resampler->Update();
GrabItkImageMemory(resampler->GetOutput(), img);
}
else
{
// !! CastToItk behaves very differently depending on the original data type
// if the target type is the same as the original, only a pointer to the data is set
// and an additional GrabItkImageMemory will cause a segfault when the image is destroyed
// GrabItkImageMemory - is not necessary in this case since we worked on the original data
// See Bug 17538.
if (img->GetPixelType().GetComponentTypeAsString() != "double")
img = GrabItkImageMemory(itkImage);
}
}
else
{
DiffusionImageType::Pointer diffImages = dynamic_cast<DiffusionImageType*>(img.GetPointer());
typedef itk::Euler3DTransform< double > RigidTransformType;
RigidTransformType::Pointer rtransform = RigidTransformType::New();
RigidTransformType::ParametersType parameters(RigidTransformType::ParametersDimension);
for (int i = 0; i<6;++i)
{
parameters[i] = transformation[i];
}
rtransform->SetParameters( parameters );
mitk::Point3D b0origin = diffImages->GetVectorImage()->GetOrigin();
b0origin[0]-=offset[0];
b0origin[1]-=offset[1];
b0origin[2]-=offset[2];
mitk::Point3D newOrigin = rtransform->GetInverseTransform()->TransformPoint(b0origin);
itk::Matrix<double,3,3> dir = diffImages->GetVectorImage()->GetDirection();
itk::Matrix<double,3,3> transM ( vnl_inverse(rtransform->GetMatrix().GetVnlMatrix()));
itk::Matrix<double,3,3> newDirection = transM * dir;
diffImages->GetVectorImage()->SetOrigin(newOrigin);
diffImages->GetVectorImage()->SetDirection(newDirection);
diffImages->Modified();
mitk::DiffusionImageCorrectionFilter<short>::Pointer correctionFilter = mitk::DiffusionImageCorrectionFilter<short>::New();
// For Diff. Images: Need to rotate the gradients (works in-place)
correctionFilter->SetImage(diffImages);
correctionFilter->CorrectDirections(transM.GetVnlMatrix());
img = diffImages;
}
}
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();
}
