ITK  5.3.0 Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration5.cxx
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceRotated10.png}
// OUTPUTS: {ImageRegistration5Output.png}
// OUTPUTS: {ImageRegistration5DifferenceAfter.png}
// OUTPUTS: {ImageRegistration5DifferenceBefore.png}
// ARGUMENTS: 0.1
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceR10X13Y17.png}
// OUTPUTS: {ImageRegistration5Output2.png}
// OUTPUTS: {ImageRegistration5DifferenceAfter2.png}
// OUTPUTS: {ImageRegistration5DifferenceBefore2.png}
// ARGUMENTS: 1.0
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{Euler2DTransform}
// for performing rigid registration in $2D$. The example code is for the
// most part identical to that presented in Section
// \ref{sec:IntroductionImageRegistration}. The main difference is the use
// of the Euler2DTransform here instead of the
// \doxygen{TranslationTransform}.
//
// \index{itk::Euler2DTransform}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// following header must also be included.
//
//
// 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 [differenceAfterRegistration] ";
std::cerr << " [differenceBeforeRegistration] ";
std::cerr << " [initialStepLength] " << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int Dimension = 2;
using PixelType = float;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
// Software Guide : BeginLatex
//
// The transform type is instantiated using the code below. The only
// template parameter for this class is the representation type of the
// space coordinates.
//
// \index{itk::Euler2DTransform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using TransformType = itk::Euler2DTransform<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
//
// In the Hello World! example, we used Fixed/Moving initial transforms
// to initialize the registration configuration. That approach was good to
// get an intuition of the registration method, specifically when we aim to
// run a multistage registration process, from which the output of each
// stage can be used to initialize the next registration stage.
//
// To get a better underestanding of the registration process in
// such situations, consider an example of 3 stages registration process
// that is started using an initial moving transform ($\Gamma_{mi}$).
// Multiple stages are handled by linking multiple instantiations of
// the \doxygen{ImageRegistrationMethodv4} class.
// Inside the registration filter of the first stage, the initial moving
// transform is added to an internal composite transform along with an
// updatable identity transform ($\Gamma_{u}$). Although the whole
// composite transform is used for metric evaluation, only the $\Gamma_{u}$
// is set to be updated by the optimizer at each iteration. The
// $\Gamma_{u}$ will be considered as the output transform of the current
// stage when the optimization process is converged. This implies that the
// output of this stage does not include the initialization parameters, so
// we need to concatenate the output and the initialization transform into
// a composite transform to be considered as the final transform of the
// first registration stage.
//
// $T_{1}(x) = \Gamma_{mi}(\Gamma_{stage_1}(x) )$
//
// Consider that, as explained in section
// \ref{sec:FeaturesOfTheRegistrationFramework}, the above transform is a
// mapping from the vitual domain (i.e. fixed image space, when no fixed
// initial transform) to the moving image space.
//
// Then, the result transform of the first stage will be used as the
// initial moving transform for the second stage of the registration
// process, and this approach goes on until the last stage of the
// registration process.
//
// At the end of the registration process, the $\Gamma_{mi}$ and the
// outputs of each stage can be concatenated into a final composite
// transform that is considered to be the final output of the whole
// registration process.
//
// $I'_{m}(x) = // I_{m}(\Gamma_{mi}(\Gamma_{stage_1}(\Gamma_{stage_2}(\Gamma_{stage_3}(x) // ) ) ) )$
//
// The above approach is especially useful if individual stages are
// characterized by different types of transforms, e.g. when we run a
// rigid registration process that is proceeded by an affine registration
// which is completed by a BSpline registration at the end.
//
//
// In addition to the above method, there is also a direct initialization
// method in which the initial transform will be optimized directly. In
// this way the initial transform will be modified during the registration
// process, so it can be used as the final transform when the registration
// process is completed. This direct approach is conceptually close to what
// was happening in ITKv3 registration.
//
// Using this method is very simple and efficient when we have only one
// level of registration, which is the case in this example. Also, a good
// application of this initialization method in a multi-stage scenario is
// when two consequent stages have the same transform types, or at least
// the initial parameters can easily be inferred from the result of the
// previous stage, such as when a translation transform is followed by a
// rigid transform.
//
// The direct initialization approach is shown by the current example in
// which we try to initialize the parameters of the optimizable transform
// ($\Gamma_{u}$) directly.
//
// For this purpose, first, 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::Euler2DTransform!New()}
// \index{itk::Euler2DTransform!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer initialTransform = TransformType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In this example, the input images are taken from readers. The code
// below updates the readers in order to ensure that the image parameters
// (size, origin and spacing) are valid when used to initialize the
// transform. We intend to use the center of the fixed image as the
// rotation center and then use the vector between the fixed image center
// and the moving image center as the initial translation to be applied
// after the rotation.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
using SpacingType = FixedImageType::SpacingType;
using OriginType = FixedImageType::PointType;
// Software Guide : BeginLatex
//
// The center of rotation is computed using the origin, size and spacing of
// the fixed image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const SpacingType fixedSpacing = fixedImage->GetSpacing();
const OriginType fixedOrigin = fixedImage->GetOrigin();
const RegionType fixedRegion = fixedImage->GetLargestPossibleRegion();
const SizeType fixedSize = fixedRegion.GetSize();
TransformType::InputPointType centerFixed;
centerFixed[0] = fixedOrigin[0] + fixedSpacing[0] * fixedSize[0] / 2.0;
centerFixed[1] = fixedOrigin[1] + fixedSpacing[1] * fixedSize[1] / 2.0;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The center of the moving image is computed in a similar way.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const SpacingType movingSpacing = movingImage->GetSpacing();
const OriginType movingOrigin = movingImage->GetOrigin();
const RegionType movingRegion = movingImage->GetLargestPossibleRegion();
const SizeType movingSize = movingRegion.GetSize();
TransformType::InputPointType centerMoving;
centerMoving[0] = movingOrigin[0] + movingSpacing[0] * movingSize[0] / 2.0;
centerMoving[1] = movingOrigin[1] + movingSpacing[1] * movingSize[1] / 2.0;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Then, we initialize the transform by
// passing the center of the fixed image as the rotation center with the
// \code{SetCenter()} method. Also, the translation is set as the vector
// relating the center of the moving image to the center of the fixed
// image. This last vector is passed with the method
// \code{SetTranslation()}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
initialTransform->SetCenter(centerFixed);
initialTransform->SetTranslation(centerMoving - centerFixed);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Let's finally initialize the rotation with a zero angle.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
initialTransform->SetAngle(0.0);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now the current parameters of the initial transform will be set
// to a registration method, so they can be assigned to the $\Gamma_{u}$
// directly. Note that you should not confuse the following function with
// the \code{SetMoving(Fixed)InitialTransform()} methods that were used in
// Hello World! example.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransform(initialTransform);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Keep in mind that the scale of units in rotation and translation is
// quite different. For example, here we know that the first element of the
// parameters array corresponds to the angle that is measured in radians,
// while the other parameters correspond to the translations that are
// measured in millimeters, so a naive application of gradient descent
// optimizer will not produce a smooth change of parameters, because a
// similar change of $\delta$ to each parameter will produce a different
// magnitude of impact on the transform. As the result, we need parameter
// scales'' to customize the learning rate for each parameter. We can take
// advantage of the scaling functionality provided by the optimizers.
//
// In this example we use small factors in the scales associated with
// translations. However, for the transforms with larger parameters
// sets, it is not intuitive for a user to set the
// scales. Fortunately, a framework for automated estimation of
// parameter scales is provided by ITKv4 that will be discussed
// later in the example of section \ref{sec:MultiStageRegistration}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales(
initialTransform->GetNumberOfParameters());
const double translationScale = 1.0 / 1000.0;
optimizerScales[0] = 1.0;
optimizerScales[1] = translationScale;
optimizerScales[2] = translationScale;
optimizer->SetScales(optimizerScales);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next we set the normal parameters of the optimization method. In this
// case we are using an \doxygen{RegularStepGradientDescentOptimizerv4}.
// Below, we define the optimization parameters like the relaxation factor,
// learning rate (initial step length), minimal step length and number of
// iterations. These last two act as stopping criteria for the
// optimization.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
double initialStepLength = 0.1;
// Software Guide : EndCodeSnippet
if (argc > 6)
{
initialStepLength = std::stod(argv[6]);
}
// Software Guide : BeginCodeSnippet
optimizer->SetRelaxationFactor(0.6);
optimizer->SetLearningRate(initialStepLength);
optimizer->SetMinimumStepLength(0.001);
optimizer->SetNumberOfIterations(200);
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
// 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 finalAngle = finalParameters[0];
const double finalTranslationX = finalParameters[1];
const double finalTranslationY = finalParameters[2];
const double rotationCenterX =
registration->GetOutput()->Get()->GetCenter()[0];
const double rotationCenterY =
registration->GetOutput()->Get()->GetCenter()[1];
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
const double bestValue = optimizer->GetValue();
// Print out results
//
const double finalAngleInDegrees = finalAngle * 180.0 / itk::Math::pi;
std::cout << "Result = " << std::endl;
std::cout << " Angle (radians) = " << finalAngle << std::endl;
std::cout << " Angle (degrees) = " << finalAngleInDegrees << std::endl;
std::cout << " Translation X = " << finalTranslationX << std::endl;
std::cout << " Translation Y = " << finalTranslationY << std::endl;
std::cout << " Fixed Center X = " << rotationCenterX << std::endl;
std::cout << " Fixed Center Y = " << rotationCenterY << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example over two of the images provided in
// \code{Examples/Data}:
//
// \begin{itemize}
// \item \code{BrainProtonDensitySliceBorder20.png}
// \item \code{BrainProtonDensitySliceRotated10.png}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first image
// by $10$ degrees around the geometrical center of the image. Both images
// have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration5}. The registration takes $17$
// iterations and produces the results:
//
// \begin{center}
// \begin{verbatim}
// [0.177612, 0.00681015, 0.00396471]
// \end{verbatim}
// \end{center}
//
// These results are interpreted as
//
// \begin{itemize}
// \item Angle = $0.177612$ radians
// \item Translation = $( 0.00681015, 0.00396471 )$ millimeters
// \end{itemize}
//
// As expected, these values match the misalignment intentionally
// introduced into the moving image quite well, since $10$ degrees is about
// $0.174532$ radians.
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceRotated10}
// \itkcaption[Rigid2D Registration input images]{Fixed and moving images
// are provided as input to the registration method using the
// CenteredRigid2D transform.} \label{fig:FixedMovingImageRegistration5}
// \end{figure}
//
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration5Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration5DifferenceBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration5DifferenceAfter}
// \itkcaption[Rigid2D Registration output images]{Resampled moving image
// (left). Differences between the fixed and moving images, before (center)
// and after (right) registration using the Euler2D transform.}
// \label{fig:ImageRegistration5Outputs}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration5Outputs} shows from left to right the
// resampled moving image after registration, the difference between the
// fixed and moving images before registration, and the difference between
// the fixed and resampled moving image after registration. It can be seen
// from the last difference image that the rotational component has been
// solved but that a small centering misalignment persists.
//
// \begin{figure}
// \center
// \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceMetric1}
// \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceAngle1}
// \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceTranslations1}
// \itkcaption[Rigid2D Registration output plots]{Metric values, rotation
// angle and translations during registration with the Euler2D
// transform.}
// \label{fig:ImageRegistration5Plots}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration5Plots} shows plots of the main output
// parameters produced from the registration process. This includes the
// metric values at every iteration, the angle values at every iteration,
// and the translation components of the transform as the registration
// progresses.
//
// Software Guide : EndLatex
using ResampleFilterType =
// TransformType::ConstPointer finalTransform = TransformType::New();
// TransformType::ConstPointer finalTransform =
// registration->GetTransform();
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform(registration->GetTransform());
resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
resample->SetOutputOrigin(fixedImage->GetOrigin());
resample->SetOutputSpacing(fixedImage->GetSpacing());
resample->SetOutputDirection(fixedImage->GetDirection());
resample->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(resample->GetOutput());
writer->SetInput(caster->GetOutput());
try
{
writer->Update();
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << "ExceptionObject while writing the resampled image !"
<< std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
using DifferenceImageType = itk::Image<float, Dimension>;
using DifferenceFilterType = itk::
SubtractImageFilter<FixedImageType, FixedImageType, DifferenceImageType>;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
intensityRescaler->SetOutputMinimum(0);
intensityRescaler->SetOutputMaximum(255);
difference->SetInput2(resample->GetOutput());
resample->SetDefaultPixelValue(1);
intensityRescaler->SetInput(difference->GetOutput());
WriterType::Pointer writer2 = WriterType::New();
writer2->SetInput(intensityRescaler->GetOutput());
try
{
// Compute the difference image between the
// fixed and moving image after registration.
if (argc > 4)
{
writer2->SetFileName(argv[4]);
writer2->Update();
}
// Compute the difference image between the
// fixed and resampled moving image after registration.
TransformType::Pointer identityTransform = TransformType::New();
identityTransform->SetIdentity();
resample->SetTransform(identityTransform);
if (argc > 5)
{
writer2->SetFileName(argv[5]);
writer2->Update();
}
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << "Error while writing difference images" << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Let's now consider the case in which rotations and translations are
// present in the initial registration, as in the following pair
// of images:
//
// \begin{itemize}
// \item \code{BrainProtonDensitySliceBorder20.png}
// \item \code{BrainProtonDensitySliceR10X13Y17.png}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first image
// by $10$ degrees and then translating it $13mm$ in $X$ and $17mm$ in $Y$.
// Both images have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration5b}. In order to accelerate
// convergence it is convenient to use a larger step length as shown here.
//
// \code{optimizer->SetMaximumStepLength( 1.3 );}
//
// The registration now takes $37$ iterations and produces the following
// results:
//
// \begin{center}
// \begin{verbatim}
// [0.174582, 13.0002, 16.0007]
// \end{verbatim}
// \end{center}
//
// These parameters are interpreted as
//
// \begin{itemize}
// \item Angle = $0.174582$ radians
// \item Translation = $( 13.0002, 16.0007 )$ millimeters
// \end{itemize}
//
// These values approximately match the initial misalignment intentionally
// introduced into the moving image, since $10$ degrees is about $0.174532$
// radians. The horizontal translation is well resolved while the vertical
// translation ends up being off by about one millimeter.
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17}
// \itkcaption[Rigid2D Registration input images]{Fixed and moving images
// provided as input to the registration method using the CenteredRigid2D
// transform.}
// \label{fig:FixedMovingImageRegistration5b}
// \end{figure}
//
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration5Output2}
// \includegraphics[width=0.32\textwidth]{ImageRegistration5DifferenceBefore2}
// \includegraphics[width=0.32\textwidth]{ImageRegistration5DifferenceAfter2}
// \itkcaption[Rigid2D Registration output images]{Resampled moving image
// (left). Differences between the fixed and moving images, before (center)
// and after (right) registration with the CenteredRigid2D transform.}
// \label{fig:ImageRegistration5Outputs2}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration5Outputs2} shows the output of the
// registration. The rightmost image of this figure shows the difference
// between the fixed image and the resampled moving image after
// registration.
//
// \begin{figure}
// \center
// \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceMetric2}
// \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceAngle2}
// \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceTranslations2}
// \itkcaption[Rigid2D Registration output plots]{Metric values, rotation
// angle and translations during the registration using the Euler2D
// transform on an image with rotation and translation mis-registration.}
// \label{fig:ImageRegistration5Plots2}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration5Plots2} shows plots of the main output
// registration parameters when the rotation and translations are combined.
// These results include the metric values at every iteration, the angle
// values at every iteration, and the translation components of the
// registration as the registration converges. It can be seen from the
// smoothness of these plots that a larger step length could have been
// supported easily by the optimizer. You may want to modify this value in
// order to get a better idea of how to tune the parameters.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itkEuler2DTransform.h
itk::GTest::TypedefsAndConstructors::Dimension2::PointType
ImageBaseType::PointType PointType
Definition: itkGTestTypedefsAndConstructors.h:51
itk::GTest::TypedefsAndConstructors::Dimension2::SizeType
ImageBaseType::SizeType SizeType
Definition: itkGTestTypedefsAndConstructors.h:49
itk::SmartPointer< Self >
itkImageRegistrationMethodv4.h
itkMeanSquaresImageToImageMetricv4.h
Data source that reads image data from a single file.
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:88
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:43
itk::GTest::TypedefsAndConstructors::Dimension2::RegionType
ImageBaseType::RegionType RegionType
Definition: itkGTestTypedefsAndConstructors.h:54
itkSubtractImageFilter.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkRescaleIntensityImageFilter.h
itk::Euler2DTransform
Euler2DTransform of a vector space (e.g. space coordinates)
Definition: itkEuler2DTransform.h:41
itkImageFileWriter.h
itk::MeanSquaresImageToImageMetricv4
Class implementing a mean squares metric.
Definition: itkMeanSquaresImageToImageMetricv4.h:46
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
itkResampleImageFilter.h
itk::Math::pi
static constexpr double pi
Definition: itkMath.h:64
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itkCommand.h
itk::Size::GetSize
const SizeValueType * GetSize() const
Definition: itkSize.h:169