ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration8.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 : BeginCommandLineArgs
// INPUTS: brainweb1e1a10f20.mha
// INPUTS: brainweb1e1a10f20Rot10Tx15.mha
// ARGUMENTS: ImageRegistration8Output.mhd
// ARGUMENTS: ImageRegistration8DifferenceBefore.mhd
// ARGUMENTS: ImageRegistration8DifferenceAfter.mhd
// OUTPUTS: {ImageRegistration8Output.png}
// OUTPUTS: {ImageRegistration8DifferenceBefore.png}
// OUTPUTS: {ImageRegistration8DifferenceAfter.png}
// OUTPUTS: {ImageRegistration8RegisteredSlice.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{VersorRigid3DTransform}
// class for performing registration of two $3D$ images.
// The class \doxygen{CenteredTransformInitializer} is used to initialize the
// center and translation of the transform. The case of rigid registration of
// 3D images is probably one of the most common uses of image registration.
//
// \index{itk::Versor\-Rigid3D\-Transform}
// \index{itk::Centered\-Transform\-Initializer!In 3D}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The following are the most relevant headers of this example.
//
// \index{itk::Versor\-Rigid3D\-Transform!header}
// \index{itk::Centered\-Transform\-Initializer!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The parameter space of the \code{VersorRigid3DTransform} is not a vector
// space, because addition is not a closed operation in the space
// of versor components. Hence, we need to use Versor composition operation
// to update the first three components of the parameter array (rotation
// parameters), and Vector addition for updating the last three components of
// the parameters array (translation
// parameters)~\cite{Hamilton1866,Joly1905}.
//
// In the previous version of ITK, a special optimizer,
// \doxygen{VersorRigid3DTransformOptimizer} was needed for registration to
// deal with versor computations. Fortunately in ITKv4, the
// \doxygen{RegularStepGradientDescentOptimizerv4} can be used for both
// vector and versor transform optimizations because, in the new registration
// framework, the task of updating parameters is delegated to the moving
// transform itself. The \code{UpdateTransformParameters} method is
// implemented in the \doxygen{Transform} class as a virtual function, and
// all the derived transform classes can have their own implementations of
// this function. Due to this fact, the updating function is re-implemented
// for versor transforms so it can handle versor composition of the rotation
// parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// The following section of code implements a Command observer
// that will 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 << optimizer->GetCurrentPosition() << 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 ";
std::cerr << " outputImagefile [differenceBeforeRegistration] ";
std::cerr << " [differenceAfterRegistration] ";
std::cerr << " [sliceBeforeRegistration] ";
std::cerr << " [sliceDifferenceBeforeRegistration] ";
std::cerr << " [sliceDifferenceAfterRegistration] ";
std::cerr << " [sliceAfterRegistration] " << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int Dimension = 3;
using PixelType = float;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
// 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
using TransformType = itk::VersorRigid3DTransform<double>;
// Software Guide : EndCodeSnippet
using MetricType =
using RegistrationType = itk::
ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType>;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
// Software Guide : BeginLatex
//
// The initial transform object is constructed below. This transform will
// be initialized, and its initial parameters will be used when the
// registration process starts.
//
// \index{itk::Versor\-Rigid3D\-Transform!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer initialTransform = TransformType::New();
// Software Guide : EndCodeSnippet
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]);
registration->SetFixedImage(fixedImageReader->GetOutput());
registration->SetMovingImage(movingImageReader->GetOutput());
// 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
// types 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
using TransformInitializerType =
FixedImageType,
MovingImageType>;
TransformInitializerType::Pointer initializer =
TransformInitializerType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The initializer is now connected to the transform and to the fixed and
// moving images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
initializer->SetTransform(initialTransform);
initializer->SetFixedImage(fixedImageReader->GetOutput());
initializer->SetMovingImage(movingImageReader->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The use of the geometrical centers is selected by calling
// \code{GeometryOn()} while the use of center of mass is selected by
// calling \code{MomentsOn()}. Below we select the center of mass mode.
//
// \index{Centered\-Transform\-Initializer!MomentsOn()}
// \index{Centered\-Transform\-Initializer!GeometryOn()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
initializer->MomentsOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, the computation of the center and translation is triggered by
// the \code{InitializeTransform()} method. The resulting values will be
// passed directly to the transform.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
initializer->InitializeTransform();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The rotation part of the transform is initialized using a
// \doxygen{Versor} which is simply a unit quaternion. The
// \code{VersorType} can be obtained from the transform traits. The versor
// itself defines the type of the vector used to indicate the rotation
// axis. This trait can be extracted as \code{VectorType}. The following
// lines create a versor object and initialize its parameters by passing a
// rotation axis and an angle.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using VersorType = TransformType::VersorType;
VersorType rotation;
VectorType axis;
axis[0] = 0.0;
axis[1] = 0.0;
axis[2] = 1.0;
constexpr double angle = 0;
rotation.Set(axis, angle);
initialTransform->SetRotation(rotation);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now the current initialized transform will be set
// to the registration method, so its initial parameters can be used to
// initialize the registration process.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransform(initialTransform);
// Software Guide : EndCodeSnippet
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales(
initialTransform->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->SetNumberOfIterations(200);
optimizer->SetLearningRate(0.2);
optimizer->SetMinimumStepLength(0.001);
optimizer->SetReturnBestParametersAndValue(true);
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
// One level registration process without shrinking and smoothing.
//
constexpr unsigned int numberOfLevels = 1;
RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
shrinkFactorsPerLevel.SetSize(1);
shrinkFactorsPerLevel[0] = 1;
RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize(1);
smoothingSigmasPerLevel[0] = 0;
registration->SetNumberOfLevels(numberOfLevels);
registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
registration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);
try
{
registration->Update();
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;
}
const TransformType::ParametersType finalParameters =
registration->GetOutput()->Get()->GetParameters();
const double versorX = finalParameters[0];
const double versorY = finalParameters[1];
const double versorZ = finalParameters[2];
const double finalTranslationX = finalParameters[3];
const double finalTranslationY = finalParameters[4];
const double finalTranslationZ = finalParameters[5];
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
const double bestValue = optimizer->GetValue();
// Print out results
//
std::cout << std::endl << std::endl;
std::cout << "Result = " << std::endl;
std::cout << " versor X = " << versorX << std::endl;
std::cout << " versor Y = " << versorY << std::endl;
std::cout << " versor Z = " << versorZ << std::endl;
std::cout << " Translation X = " << finalTranslationX << std::endl;
std::cout << " Translation Y = " << finalTranslationY << std::endl;
std::cout << " Translation Z = " << finalTranslationZ << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example over some of the images available in the
// following website
//
// \url{http://public.kitware.com/pub/itk/Data/BrainWeb}.
//
// Note that the images in this website are compressed in \code{.tgz}
// files. You should download these files and decompress 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 $X$.
//
// Also, instead of doing the above steps manually, you can turn on the
// following flag in your build environment:
//
// \code{ITK\_USE\_BRAINWEB\_DATA}
//
// Then, the above data will be loaded to your local ITK build directory.
//
// The registration takes $21$ iterations and produces:
//
// \begin{center}
// \begin{verbatim}
// [7.2295e-05, -7.20626e-05, -0.0872168, 2.64765, -17.4626, -0.00147153]
// \end{verbatim}
// \end{center}
//
// That are interpreted as
//
// \begin{itemize}
// \item Versor = $(7.2295e-05, -7.20626e-05, -0.0872168)$
// \item Translation = $(2.64765, -17.4626, -0.00147153)$ 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 from the $6$ parameters.
//
// Software Guide : EndLatex
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetFixedParameters(
registration->GetOutput()->Get()->GetFixedParameters());
finalTransform->SetParameters(finalParameters);
// Software Guide : BeginCodeSnippet
TransformType::MatrixType matrix = finalTransform->GetMatrix();
TransformType::OffsetType offset = finalTransform->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 =
// 0.984786 0.173769 -0.000156187
// -0.173769 0.984786 -0.000131469
// 0.000130965 0.000156609 1
//
// Offset =
// [-15, 0.0189186, -0.0305439]
// \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
// $\arcsin{(0.173769)}$ 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 Z component of the versor is plotted as an indication of
// how the rotation progresses. 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
// \code{ITKSoftwareGuide} Git repository under the directory
//
// \code{ITKSoftwareGuide/SoftwareGuide/Art}.
//
// You are strongly encouraged to run the example code, since only in this
// way can you gain 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
using ResampleFilterType =
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);
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using CastFilterType =
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName(argv[3]);
caster->SetInput(resampler->GetOutput());
writer->SetInput(caster->GetOutput());
writer->Update();
using DifferenceFilterType =
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
using 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();
}
using IdentityTransformType = itk::IdentityTransform<double, Dimension>;
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.
//
//
using OutputSliceType = itk::Image<OutputPixelType, 2>;
using ExtractFilterType =
ExtractFilterType::Pointer extractor = ExtractFilterType::New();
extractor->SetDirectionCollapseToSubmatrix();
extractor->InPlaceOn();
fixedImage->GetLargestPossibleRegion();
FixedImageType::SizeType size = inputRegion.GetSize();
FixedImageType::IndexType start = inputRegion.GetIndex();
// Select one slice as output
size[2] = 0;
start[2] = 90;
desiredRegion.SetSize(size);
desiredRegion.SetIndex(start);
extractor->SetExtractionRegion(desiredRegion);
using SliceWriterType = itk::ImageFileWriter<OutputSliceType>;
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;
}
itkExtractImageFilter.h
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itkRegularStepGradientDescentOptimizerv4.h
itk::IdentityTransform
Implementation of an Identity Transform.
Definition: itkIdentityTransform.h:50
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
itkImageFileReader.h
itk::GTest::TypedefsAndConstructors::Dimension2::SizeType
ImageBaseType::SizeType SizeType
Definition: itkGTestTypedefsAndConstructors.h:49
itk::SmartPointer< Self >
itkCastImageFilter.h
itkImageRegistrationMethodv4.h
itkMeanSquaresImageToImageMetricv4.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::RegularStepGradientDescentOptimizerv4
Regular Step Gradient descent optimizer.
Definition: itkRegularStepGradientDescentOptimizerv4.h:47
itk::GTest::TypedefsAndConstructors::Dimension2::IndexType
ImageBaseType::IndexType IndexType
Definition: itkGTestTypedefsAndConstructors.h:50
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::Index::GetIndex
const IndexValueType * GetIndex() const
Definition: itkIndex.h:228
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:43
itk::GTest::TypedefsAndConstructors::Dimension2::RegionType
ImageBaseType::RegionType RegionType
Definition: itkGTestTypedefsAndConstructors.h:54
itkVersorRigid3DTransform.h
itkSubtractImageFilter.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkRescaleIntensityImageFilter.h
itk::SubtractImageFilter
Pixel-wise subtraction of two images.
Definition: itkSubtractImageFilter.h:68
itkImageFileWriter.h
itk::MeanSquaresImageToImageMetricv4
Class implementing a mean squares metric.
Definition: itkMeanSquaresImageToImageMetricv4.h:46
itk::ExtractImageFilter
Decrease the image size by cropping the image to the selected region bounds.
Definition: itkExtractImageFilter.h:118
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::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
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::SetSize
void SetSize(const SizeType &size)
Definition: itkImageRegion.h:174
itkResampleImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itkCommand.h
itk::CenteredTransformInitializer
CenteredTransformInitializer is a helper class intended to initialize the center of rotation and the ...
Definition: itkCenteredTransformInitializer.h:61
itk::Size::GetSize
const SizeValueType * GetSize() const
Definition: itkSize.h:169