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 runKernelTransform(MatlabImportFilter::Pointer matlabImport,
MatlabExportFilter::Pointer matlabExport) {
// check number of input arguments (the kernel transform syntax
// accepts up to 4 arguments only. Thus, we cannot use InputIndexType_MAX)
matlabImport->CheckNumberOfArguments(4, 4);
// retrieve pointers to the inputs that we are going to need here
typedef MatlabImportFilter::MatlabInputPointer MatlabInputPointer;
MatlabInputPointer inX = matlabImport->GetRegisteredInput("X");
MatlabInputPointer inY = matlabImport->GetRegisteredInput("Y");
MatlabInputPointer inXI = matlabImport->GetRegisteredInput("XI");
// register the outputs for this function at the export filter
typedef MatlabExportFilter::MatlabOutputPointer MatlabOutputPointer;
MatlabOutputPointer outYI = matlabExport->RegisterOutput(OUT_YI, "YI");
// get size of input arguments
mwSize Mx = mxGetM(inX->pm); // number of source points
mwSize Mxi = mxGetM(inXI->pm); // number of points to be warped
mwSize ndimxi; // number of dimension of points to be warped
const mwSize *dimsxi; // dimensions vector of array of points to be warped
// create pointers to input matrices
TScalarType *x
= (TScalarType *)mxGetData(inX->pm); // source points
TScalarType *y
= (TScalarType *)mxGetData(inY->pm); // target points
TScalarType *xi
= (TScalarType *)mxGetData(inXI->pm); // points to be warped
if (x == NULL) {
mexErrMsgTxt("Cannot get a pointer to input X");
}
if (y == NULL) {
mexErrMsgTxt("Cannot get a pointer to input Y");
}
if (xi == NULL) {
mexErrMsgTxt("Cannot get a pointer to input XI");
}
// type definitions and variables to store points for the kernel transform
typedef typename TransformType::PointSetType PointSetType;
typename PointSetType::Pointer fixedPointSet = PointSetType::New();
typename PointSetType::Pointer movingPointSet = PointSetType::New();
typename PointSetType::Pointer toWarpPointSet = PointSetType::New();
typedef typename PointSetType::PointsContainer PointsContainer;
typename PointsContainer::Pointer fixedPointContainer = PointsContainer::New();
typename PointsContainer::Pointer movingPointContainer = PointsContainer::New();
typedef typename PointSetType::PointType PointType;
PointType fixedPoint;
PointType movingPoint;
PointType toWarpPoint;
PointType warpedPoint;
// duplicate the input x and y matrices to PointSet format so that
// we can pass it to the ITK function
mwSize pointId=0;
for (mwSize row=0; row < Mx; ++row) {
for (mwSize col=0; col < (mwSize)Dimension; ++col) {
fixedPoint[CAST2MWSIZE(col)] = y[Mx * col + row];
movingPoint[CAST2MWSIZE(col)] = x[Mx * col + row];
}
fixedPointContainer->InsertElement(pointId, fixedPoint);
movingPointContainer->InsertElement(pointId, movingPoint);
++pointId;
}
fixedPointSet->SetPoints(fixedPointContainer);
movingPointSet->SetPoints(movingPointContainer);
// compute the transform
typename TransformType::Pointer transform;
transform = TransformType::New();
transform->SetSourceLandmarks(movingPointSet);
transform->SetTargetLandmarks(fixedPointSet);
transform->ComputeWMatrix();
// create output vector and pointer to populate it
ndimxi = mxGetNumberOfDimensions(inXI->pm);
dimsxi = mxGetDimensions(inXI->pm);
std::vector<mwSize> size;
for (mwIndex i = 0; i < ndimxi; ++i) {
size.push_back(dimsxi[i]);
}
TScalarType *yi
= matlabExport->AllocateNDArrayInMatlab<TScalarType>(outYI, size);
// transform points
for (mwSize row=0; row < Mxi; ++row) {
for (mwSize col=0; col < (mwSize)Dimension; ++col) {
toWarpPoint[CAST2MWSIZE(col)] = xi[Mxi * col + row];
}
warpedPoint = transform->TransformPoint(toWarpPoint);
for (mwSize col=0; col < (mwSize)Dimension; ++col) {
yi[Mxi * col + row] = warpedPoint[CAST2MWSIZE(col)];
}
}
// exit function
return;
}
