ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/DeformableRegistration15.cxx
/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
// Software Guide : BeginLatex
//
// This example illustrates a realistic pipeline for solving a full deformable
// registration problem.
//
// First the two images are roughly aligned by using a transform
// initialization, then they are registered using a rigid transform, that in
// turn, is used to initialize a registration with an affine transform. The
// transform resulting from the affine registration is compounded with
// a BSplineTransform. The deformable registration is computed,
// and finally the resulting transform is used to resample the moving image.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The following are the most relevant headers to this example.
//
// \index{itk::VersorRigid3DTransform!header}
// \index{itk::AffineTransform!header}
// \index{itk::BSplineTransform!header}
// \index{itk::RegularStepGradientDescentOptimizer!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// The following section of code implements a Command observer
// used to monitor the evolution of the registration process.
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
CommandIterationUpdate() = default;
public:
using OptimizerPointer = const OptimizerType *;
void
Execute(itk::Object * caller, const itk::EventObject & event) override
{
Execute((const itk::Object *)caller, event);
}
void
Execute(const itk::Object * object, const itk::EventObject & event) override
{
auto optimizer = static_cast<OptimizerPointer>(object);
if (!(itk::IterationEvent().CheckEvent(&event)))
{
return;
}
std::cout << optimizer->GetCurrentIteration() << " ";
std::cout << optimizer->GetValue() << " ";
std::cout << std::endl;
}
};
int
main(int argc, char * argv[])
{
if (argc < 4)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile outputImagefile ";
std::cerr << " [differenceOutputfile] [differenceBeforeRegistration] ";
std::cerr << " [filenameForFinalTransformParameters] ";
std::cerr << " [useExplicitPDFderivatives ] [useCachingBSplineWeights ] ";
std::cerr << " [deformationField] ";
std::cerr << " [numberOfGridNodesInsideImageInOneDimensionCoarse] ";
std::cerr << " [numberOfGridNodesInsideImageInOneDimensionFine] ";
std::cerr << " [maximumStepLength] [maximumNumberOfIterations]";
std::cerr << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int ImageDimension = 3;
using PixelType = signed short;
using FixedImageType = itk::Image<PixelType, ImageDimension>;
using MovingImageType = itk::Image<PixelType, ImageDimension>;
const unsigned int SpaceDimension = ImageDimension;
constexpr unsigned int SplineOrder = 3;
using CoordinateRepType = double;
using RigidTransformType = itk::VersorRigid3DTransform<double>;
using AffineTransformType = itk::AffineTransform<double, SpaceDimension>;
using DeformableTransformType =
using TransformInitializerType =
FixedImageType,
MovingImageType>;
using MetricType =
MovingImageType>;
using InterpolatorType =
using 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);
// Auxiliary identity transform.
using IdentityTransformType =
IdentityTransformType::Pointer identityTransform =
IdentityTransformType::New();
// Read the Fixed and Moving images.
using FixedImageReaderType = itk::ImageFileReader<FixedImageType>;
using MovingImageReaderType = itk::ImageFileReader<MovingImageType>;
FixedImageReaderType::Pointer fixedImageReader =
FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader =
MovingImageReaderType::New();
fixedImageReader->SetFileName(argv[1]);
movingImageReader->SetFileName(argv[2]);
try
{
fixedImageReader->Update();
movingImageReader->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
registration->SetFixedImage(fixedImage);
registration->SetMovingImage(movingImageReader->GetOutput());
// Add a time and memory probes collector for profiling the computation time
// of every stage.
// Setup the metric parameters
metric->SetNumberOfHistogramBins(50);
FixedImageType::RegionType fixedRegion = fixedImage->GetBufferedRegion();
const unsigned int numberOfPixels = fixedRegion.GetNumberOfPixels();
metric->ReinitializeSeed(76926294);
if (argc > 7)
{
// 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(std::stoi(argv[7]));
}
if (argc > 8)
{
// Define whether to cache the BSpline weights and indexes corresponding
// to each one of the samples used to compute the metric. Enabling caching
// will make the algorithm run faster but it will have a cost on the
// amount of memory that needs to be allocated. This option is only
// relevant when using the BSplineTransform.
metric->SetUseCachingOfBSplineWeights(std::stoi(argv[8]));
}
// Initialize a rigid transform by using Image Intensity Moments
TransformInitializerType::Pointer initializer =
TransformInitializerType::New();
RigidTransformType::Pointer rigidTransform = RigidTransformType::New();
initializer->SetTransform(rigidTransform);
initializer->SetFixedImage(fixedImageReader->GetOutput());
initializer->SetMovingImage(movingImageReader->GetOutput());
initializer->MomentsOn();
std::cout << "Starting Rigid Transform Initialization " << std::endl;
memorymeter.Start("Rigid Initialization");
chronometer.Start("Rigid Initialization");
initializer->InitializeTransform();
chronometer.Stop("Rigid Initialization");
memorymeter.Stop("Rigid Initialization");
std::cout << "Rigid Transform Initialization completed" << std::endl;
std::cout << std::endl;
registration->SetFixedImageRegion(fixedRegion);
registration->SetInitialTransformParameters(
rigidTransform->GetParameters());
registration->SetTransform(rigidTransform);
// Define optimizer normalization to compensate for different dynamic range
// of rotations and translations.
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales(
rigidTransform->GetNumberOfParameters());
const double translationScale = 1.0 / 1000.0;
optimizerScales[0] = 1.0;
optimizerScales[1] = 1.0;
optimizerScales[2] = 1.0;
optimizerScales[3] = translationScale;
optimizerScales[4] = translationScale;
optimizerScales[5] = translationScale;
optimizer->SetScales(optimizerScales);
optimizer->SetMaximumStepLength(0.2000);
optimizer->SetMinimumStepLength(0.0001);
optimizer->SetNumberOfIterations(200);
//
// The rigid transform has 6 parameters we use therefore a few samples to
// run this stage.
//
// Regulating the number of samples in the Metric is equivalent to
// performing multi-resolution registration because it is indeed a
// sub-sampling of the image.
metric->SetNumberOfSpatialSamples(10000L);
// Create the Command observer and register it with the optimizer.
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
std::cout << "Starting Rigid Registration " << std::endl;
try
{
memorymeter.Start("Rigid Registration");
chronometer.Start("Rigid Registration");
registration->Update();
chronometer.Stop("Rigid Registration");
memorymeter.Stop("Rigid Registration");
std::cout << "Optimizer stop condition = "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
std::cout << "Rigid Registration completed" << std::endl;
std::cout << std::endl;
rigidTransform->SetParameters(registration->GetLastTransformParameters());
// Perform Affine Registration
AffineTransformType::Pointer affineTransform = AffineTransformType::New();
affineTransform->SetCenter(rigidTransform->GetCenter());
affineTransform->SetTranslation(rigidTransform->GetTranslation());
affineTransform->SetMatrix(rigidTransform->GetMatrix());
registration->SetTransform(affineTransform);
registration->SetInitialTransformParameters(
affineTransform->GetParameters());
optimizerScales =
OptimizerScalesType(affineTransform->GetNumberOfParameters());
optimizerScales[0] = 1.0;
optimizerScales[1] = 1.0;
optimizerScales[2] = 1.0;
optimizerScales[3] = 1.0;
optimizerScales[4] = 1.0;
optimizerScales[5] = 1.0;
optimizerScales[6] = 1.0;
optimizerScales[7] = 1.0;
optimizerScales[8] = 1.0;
optimizerScales[9] = translationScale;
optimizerScales[10] = translationScale;
optimizerScales[11] = translationScale;
optimizer->SetScales(optimizerScales);
optimizer->SetMaximumStepLength(0.2000);
optimizer->SetMinimumStepLength(0.0001);
optimizer->SetNumberOfIterations(200);
// The Affine transform has 12 parameters we use therefore a more samples to
// run this stage.
//
// Regulating the number of samples in the Metric is equivalent to
// performing multi-resolution registration because it is indeed a
// sub-sampling of the image.
metric->SetNumberOfSpatialSamples(50000L);
std::cout << "Starting Affine Registration " << std::endl;
try
{
memorymeter.Start("Affine Registration");
chronometer.Start("Affine Registration");
registration->Update();
chronometer.Stop("Affine Registration");
memorymeter.Stop("Affine Registration");
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
std::cout << "Affine Registration completed" << std::endl;
std::cout << std::endl;
affineTransform->SetParameters(registration->GetLastTransformParameters());
// Perform Deformable Registration
DeformableTransformType::Pointer bsplineTransformCoarse =
DeformableTransformType::New();
unsigned int numberOfGridNodesInOneDimensionCoarse = 5;
DeformableTransformType::PhysicalDimensionsType fixedPhysicalDimensions;
DeformableTransformType::MeshSizeType meshSize;
DeformableTransformType::OriginType fixedOrigin;
for (unsigned int i = 0; i < SpaceDimension; i++)
{
fixedOrigin[i] = fixedImage->GetOrigin()[i];
fixedPhysicalDimensions[i] =
fixedImage->GetSpacing()[i] *
static_cast<double>(
fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1);
}
meshSize.Fill(numberOfGridNodesInOneDimensionCoarse - SplineOrder);
bsplineTransformCoarse->SetTransformDomainOrigin(fixedOrigin);
bsplineTransformCoarse->SetTransformDomainPhysicalDimensions(
fixedPhysicalDimensions);
bsplineTransformCoarse->SetTransformDomainMeshSize(meshSize);
bsplineTransformCoarse->SetTransformDomainDirection(
fixedImage->GetDirection());
using ParametersType = DeformableTransformType::ParametersType;
unsigned int numberOfBSplineParameters =
bsplineTransformCoarse->GetNumberOfParameters();
optimizerScales = OptimizerScalesType(numberOfBSplineParameters);
optimizerScales.Fill(1.0);
optimizer->SetScales(optimizerScales);
ParametersType initialDeformableTransformParameters(
numberOfBSplineParameters);
initialDeformableTransformParameters.Fill(0.0);
using CompositeTransformType =
typename CompositeTransformType::Pointer compositeTransform =
CompositeTransformType::New();
compositeTransform->AddTransform(affineTransform);
compositeTransform->AddTransform(bsplineTransformCoarse);
compositeTransform->SetOnlyMostRecentTransformToOptimizeOn();
bsplineTransformCoarse->SetParameters(initialDeformableTransformParameters);
registration->SetInitialTransformParameters(
bsplineTransformCoarse->GetParameters());
registration->SetTransform(compositeTransform);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next we set the parameters of the RegularStepGradientDescentOptimizer
// object.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetMaximumStepLength(10.0);
optimizer->SetMinimumStepLength(0.01);
optimizer->SetRelaxationFactor(0.7);
optimizer->SetNumberOfIterations(50);
// Software Guide : EndCodeSnippet
// Optionally, get the step length from the command line arguments
if (argc > 11)
{
optimizer->SetMaximumStepLength(std::stod(argv[12]));
}
// Optionally, get the number of iterations from the command line arguments
if (argc > 12)
{
optimizer->SetNumberOfIterations(std::stoi(argv[13]));
}
// The BSpline transform has a large number of parameters, we use therefore
// a much larger number of samples to run this stage.
//
// Regulating the number of samples in the Metric is equivalent to
// performing multi-resolution registration because it is indeed a
// sub-sampling of the image.
metric->SetNumberOfSpatialSamples(numberOfBSplineParameters * 100);
std::cout << std::endl
<< "Starting Deformable Registration Coarse Grid" << std::endl;
try
{
memorymeter.Start("Deformable Registration Coarse");
chronometer.Start("Deformable Registration Coarse");
registration->Update();
chronometer.Stop("Deformable Registration Coarse");
memorymeter.Stop("Deformable Registration Coarse");
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
std::cout << "Deformable Registration Coarse Grid completed" << std::endl;
std::cout << std::endl;
OptimizerType::ParametersType finalParameters =
registration->GetLastTransformParameters();
bsplineTransformCoarse->SetParameters(finalParameters);
// Software Guide : BeginLatex
//
// Once the registration has finished with the low resolution grid, we
// proceed to instantiate a higher resolution
// \code{BSplineTransform}.
//
// Software Guide : EndLatex
DeformableTransformType::Pointer bsplineTransformFine =
DeformableTransformType::New();
unsigned int numberOfGridNodesInOneDimensionFine = 20;
meshSize.Fill(numberOfGridNodesInOneDimensionFine - SplineOrder);
bsplineTransformFine->SetTransformDomainOrigin(fixedOrigin);
bsplineTransformFine->SetTransformDomainPhysicalDimensions(
fixedPhysicalDimensions);
bsplineTransformFine->SetTransformDomainMeshSize(meshSize);
bsplineTransformFine->SetTransformDomainDirection(
fixedImage->GetDirection());
numberOfBSplineParameters = bsplineTransformFine->GetNumberOfParameters();
ParametersType parametersHigh(numberOfBSplineParameters);
parametersHigh.Fill(0.0);
// Software Guide : BeginLatex
//
// Now we need to initialize the BSpline coefficients of the higher
// resolution transform. This is done by first computing the actual
// deformation field at the higher resolution from the lower resolution
// BSpline coefficients. Then a BSpline decomposition is done to obtain the
// BSpline coefficient of the higher resolution transform.
//
// Software Guide : EndLatex
unsigned int counter = 0;
for (unsigned int k = 0; k < SpaceDimension; k++)
{
using ParametersImageType = DeformableTransformType::ImageType;
using ResamplerType =
ResamplerType::Pointer upsampler = ResamplerType::New();
using FunctionType =
FunctionType::Pointer function = FunctionType::New();
upsampler->SetInput(bsplineTransformCoarse->GetCoefficientImages()[k]);
upsampler->SetInterpolator(function);
upsampler->SetTransform(identityTransform);
upsampler->SetSize(bsplineTransformFine->GetCoefficientImages()[k]
->GetLargestPossibleRegion()
.GetSize());
upsampler->SetOutputSpacing(
bsplineTransformFine->GetCoefficientImages()[k]->GetSpacing());
upsampler->SetOutputOrigin(
bsplineTransformFine->GetCoefficientImages()[k]->GetOrigin());
using DecompositionType =
ParametersImageType>;
DecompositionType::Pointer decomposition = DecompositionType::New();
decomposition->SetSplineOrder(SplineOrder);
decomposition->SetInput(upsampler->GetOutput());
decomposition->Update();
ParametersImageType::Pointer newCoefficients = decomposition->GetOutput();
// copy the coefficients into the parameter array
Iterator it(newCoefficients,
bsplineTransformFine->GetCoefficientImages()[k]
->GetLargestPossibleRegion());
while (!it.IsAtEnd())
{
parametersHigh[counter++] = it.Get();
++it;
}
}
optimizerScales = OptimizerScalesType(numberOfBSplineParameters);
optimizerScales.Fill(1.0);
optimizer->SetScales(optimizerScales);
bsplineTransformFine->SetParameters(parametersHigh);
// Software Guide : BeginLatex
//
// We now pass the parameters of the high resolution transform as the
// initial parameters to be used in a second stage of the registration
// process.
//
// Software Guide : EndLatex
std::cout << "Starting Registration with high resolution transform"
<< std::endl;
// Software Guide : BeginCodeSnippet
compositeTransform->RemoveTransform(); // remove bsplineTransformCoarse
compositeTransform->AddTransform(bsplineTransformFine);
compositeTransform->SetOnlyMostRecentTransformToOptimizeOn();
registration->SetInitialTransformParameters(
bsplineTransformFine->GetParameters());
//
// The BSpline transform at fine scale has a very large number of
// parameters, we use therefore a much larger number of samples to run this
// stage. In this case, however, the number of transform parameters is
// closer to the number of pixels in the image. Therefore we use the
// geometric mean of the two numbers to ensure that the number of samples is
// larger than the number of transform parameters and smaller than the
// number of samples.
//
// Regulating the number of samples in the Metric is equivalent to
// performing multi-resolution registration because it is indeed a
// sub-sampling of the image.
const auto numberOfSamples = static_cast<unsigned long>(
std::sqrt(static_cast<double>(numberOfBSplineParameters) *
static_cast<double>(numberOfPixels)));
metric->SetNumberOfSpatialSamples(numberOfSamples);
try
{
memorymeter.Start("Deformable Registration Fine");
chronometer.Start("Deformable Registration Fine");
registration->Update();
chronometer.Stop("Deformable Registration Fine");
memorymeter.Stop("Deformable Registration Fine");
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
std::cout << "Deformable Registration Fine Grid completed" << std::endl;
std::cout << std::endl;
// Report the time and memory taken by the registration
chronometer.Report(std::cout);
memorymeter.Report(std::cout);
finalParameters = registration->GetLastTransformParameters();
bsplineTransformFine->SetParameters(finalParameters);
using ResampleFilterType =
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform(bsplineTransformFine);
resample->SetInput(movingImageReader->GetOutput());
resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
resample->SetOutputOrigin(fixedImage->GetOrigin());
resample->SetOutputSpacing(fixedImage->GetSpacing());
resample->SetOutputDirection(fixedImage->GetDirection());
// This value is set to zero in order to make easier to perform
// regression testing in this example. However, for didactic
// exercise it will be better to set it to a medium gray value
// such as 100 or 128.
resample->SetDefaultPixelValue(0);
using OutputPixelType = signed short;
using CastFilterType =
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName(argv[3]);
caster->SetInput(resample->GetOutput());
writer->SetInput(caster->GetOutput());
std::cout << "Writing resampled moving image...";
try
{
writer->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
std::cout << " Done!" << std::endl;
using DifferenceFilterType =
FixedImageType,
OutputImageType>;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
using SqrtFilterType =
SqrtFilterType::Pointer sqrtFilter = SqrtFilterType::New();
sqrtFilter->SetInput(difference->GetOutput());
using DifferenceImageWriterType = itk::ImageFileWriter<OutputImageType>;
DifferenceImageWriterType::Pointer writer2 =
DifferenceImageWriterType::New();
writer2->SetInput(sqrtFilter->GetOutput());
// Compute the difference image between the
// fixed and resampled moving image.
if (argc > 4)
{
difference->SetInput1(fixedImageReader->GetOutput());
difference->SetInput2(resample->GetOutput());
writer2->SetFileName(argv[4]);
std::cout << "Writing difference image after registration...";
try
{
writer2->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
std::cout << " Done!" << std::endl;
}
// Compute the difference image between the
// fixed and moving image before registration.
if (argc > 5)
{
writer2->SetFileName(argv[5]);
difference->SetInput1(fixedImageReader->GetOutput());
resample->SetTransform(identityTransform);
std::cout << "Writing difference image before registration...";
try
{
writer2->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
std::cout << " Done!" << std::endl;
}
// Generate the explicit deformation field resulting from
// the registration.
if (argc > 9)
{
using DisplacementFieldType = itk::Image<VectorType, ImageDimension>;
DisplacementFieldType::Pointer field = DisplacementFieldType::New();
field->SetRegions(fixedRegion);
field->SetOrigin(fixedImage->GetOrigin());
field->SetSpacing(fixedImage->GetSpacing());
field->SetDirection(fixedImage->GetDirection());
field->Allocate();
FieldIterator fi(field, fixedRegion);
fi.GoToBegin();
DeformableTransformType::InputPointType fixedPoint;
DeformableTransformType::OutputPointType movingPoint;
VectorType displacement;
while (!fi.IsAtEnd())
{
index = fi.GetIndex();
field->TransformIndexToPhysicalPoint(index, fixedPoint);
movingPoint = bsplineTransformFine->TransformPoint(fixedPoint);
displacement = movingPoint - fixedPoint;
fi.Set(displacement);
++fi;
}
FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
fieldWriter->SetInput(field);
fieldWriter->SetFileName(argv[9]);
std::cout << "Writing deformation field ...";
try
{
fieldWriter->Update();
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << "Exception thrown " << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
std::cout << " Done!" << std::endl;
}
// Optionally, save the transform parameters in a file
if (argc > 6)
{
std::cout << "Writing transform parameter file ...";
using TransformWriterType = itk::TransformFileWriter;
TransformWriterType::Pointer transformWriter = TransformWriterType::New();
transformWriter->AddTransform(bsplineTransformFine);
transformWriter->SetFileName(argv[6]);
transformWriter->Update();
std::cout << " Done!" << std::endl;
}
return EXIT_SUCCESS;
}
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itkTimeProbesCollectorBase.h
itk::SquaredDifferenceImageFilter
Implements pixel-wise the computation of squared difference.
Definition: itkSquaredDifferenceImageFilter.h:88
itk::CompositeTransform
This class contains a list of transforms and concatenates them by composition.
Definition: itkCompositeTransform.h:87
itk::TransformFileWriter
itk::TransformFileWriterTemplate< double > TransformFileWriter
Definition: itkTransformFileWriter.h:129
itk::IdentityTransform
Implementation of an Identity Transform.
Definition: itkIdentityTransform.h:50
itk::BSplineDecompositionImageFilter
Calculates the B-Spline coefficients of an image. Spline order may be from 0 to 5.
Definition: itkBSplineDecompositionImageFilter.h:74
itk::ResourceProbesCollectorBase::Start
virtual void Start(const char *name)
itkRegularStepGradientDescentOptimizer.h
itk::VersorRigid3DTransform
VersorRigid3DTransform of a vector space (e.g. space coordinates)
Definition: itkVersorRigid3DTransform.h:46
itkCenteredTransformInitializer.h
itk::GTest::TypedefsAndConstructors::Dimension2::VectorType
ImageBaseType::SpacingType VectorType
Definition: itkGTestTypedefsAndConstructors.h:53
itk::MemoryProbesCollectorBase
Aggregates a set of memory probes.
Definition: itkMemoryProbesCollectorBase.h:37
itk::Vector
A templated class holding a n-Dimensional vector.
Definition: itkVector.h:62
itkImageFileReader.h
itk::ImageRegistrationMethod
Base class for Image Registration Methods.
Definition: itkImageRegistrationMethod.h:70
itkSqrtImageFilter.h
itk::ResourceProbesCollectorBase::Report
virtual void Report(std::ostream &os=std::cout, bool printSystemInfo=true, bool printReportHead=true, bool useTabs=false)
itk::SmartPointer< Self >
itkCastImageFilter.h
itkAffineTransform.h
itk::AffineTransform
Definition: itkAffineTransform.h:101
itk::SqrtImageFilter
Computes the square root of each pixel.
Definition: itkSqrtImageFilter.h:71
itk::RegularStepGradientDescentOptimizer
Implement a gradient descent optimizer.
Definition: itkRegularStepGradientDescentOptimizer.h:33
itkMemoryProbesCollectorBase.h
itkBSplineResampleImageFunction.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::ImageRegionIterator
A multi-dimensional iterator templated over image type that walks a region of pixels.
Definition: itkImageRegionIterator.h:78
itkBSplineDecompositionImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::IndexType
ImageBaseType::IndexType IndexType
Definition: itkGTestTypedefsAndConstructors.h:50
itk::LinearInterpolateImageFunction
Linearly interpolate an image at specified positions.
Definition: itkLinearInterpolateImageFunction.h:50
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itk::BSplineTransform
Deformable transform using a BSpline representation.
Definition: itkBSplineTransform.h:103
itk::BSplineResampleImageFunction
Resample image intensity from a BSpline coefficient image.
Definition: itkBSplineResampleImageFunction.h:57
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkBSplineTransform.h
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:43
itk::GTest::TypedefsAndConstructors::Dimension2::RegionType
ImageBaseType::RegionType RegionType
Definition: itkGTestTypedefsAndConstructors.h:54
itkImageRegistrationMethod.h
itkVersorRigid3DTransform.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkImageFileWriter.h
itkSquaredDifferenceImageFilter.h
itk::ImageConstIterator::Get
PixelType Get() const
Definition: itkImageConstIterator.h:343
itk::ResampleImageFilter
Resample an image via a coordinate transform.
Definition: itkResampleImageFilter.h:90
itk::Object
Base class for most ITK classes.
Definition: itkObject.h:62
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::EventObject
Abstraction of the Events used to communicating among filters and with GUIs.
Definition: itkEventObject.h:57
itk::ImageRegion::GetNumberOfPixels
SizeValueType GetNumberOfPixels() const
itkCompositeTransform.h
itkResampleImageFilter.h
itkTransformFileWriter.h
itkCommand.h
itk::ResourceProbesCollectorBase::Stop
virtual void Stop(const char *name)
itkMattesMutualInformationImageToImageMetric.h
itk::CenteredTransformInitializer
CenteredTransformInitializer is a helper class intended to initialize the center of rotation and the ...
Definition: itkCenteredTransformInitializer.h:61
itk::TimeProbesCollectorBase
Aggregates a set of time probes.
Definition: itkTimeProbesCollectorBase.h:38
itk::MattesMutualInformationImageToImageMetric
Computes the mutual information between two images to be registered using the method of Mattes et al.
Definition: itkMattesMutualInformationImageToImageMetric.h:116