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