// 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());
}
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();
}
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 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();
}
