ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration6.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
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*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceR10X13Y17.png}
// OUTPUTS: {ImageRegistration6Output.png}
// OUTPUTS: {ImageRegistration6DifferenceBefore.png}
// OUTPUTS: {ImageRegistration6DifferenceAfter.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{Euler2DTransform}
// for performing registration. The example code is for the most part
// identical to the one presented in Section~\ref{sec:RigidRegistrationIn2D}.
// Even though this current example is done in $2D$, the class
// \doxygen{CenteredTransformInitializer} is quite generic and could be used
// in other dimensions. The objective of the initializer class is to simplify
// the computation of the center of rotation and the translation required to
// initialize certain transforms such as the
// Euler2DTransform. The initializer accepts two images and
// a transform as inputs. The images are considered to be the fixed and
// moving images of the registration problem, while the transform is the one
// used to register the images.
//
// The CenteredTransformInitializer supports two modes of operation. In the
// first mode, the centers of the images are computed as space coordinates
// using the image origin, size and spacing. The center of the fixed image is
// assigned as the rotational center of the transform while the vector going
// from the fixed image center to the moving image center is passed as the
// initial translation of the transform. In the second mode, the image centers
// are not computed geometrically but by using the moments of the intensity
// gray levels. The center of mass of each image is computed using the helper
// class \doxygen{ImageMomentsCalculator}. The center of mass of the fixed
// image is passed as the rotational center of the transform while the vector
// going from the fixed image center of mass to the moving image center of
// mass is passed as the initial translation of the transform. This second
// mode of operation is quite convenient when the anatomical structures of
// interest are not centered in the image. In such cases the alignment of the
// centers of mass provides a better rough initial registration than the
// simple use of the geometrical centers. The validity of the initial
// registration should be questioned when the two images are acquired in
// different imaging modalities. In those cases, the center of mass of
// intensities in one modality does not necessarily match the center of mass
// of intensities in the other imaging modality.
//
// \index{itk::Euler2DTransform}
// \index{itk::ImageMomentsCalculator}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The following are the most relevant headers in this example.
//
// \index{itk::Euler2DTransform!header}
//
// 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::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 of 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 =
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
// Software Guide : BeginLatex
//
// Like the previous section, a direct initialization method is used here.
// The transform object is constructed below. This transform will
// be initialized, and its initial parameters will be considered as
// the parameters to be used when the registration process begins.
//
// \index{itk::Euler2DTransform!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = 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 to
// explicitly call \code{Update()} on the readers since the
// CenteredTransformInitializer class will do it as part of its
// initialization. The following code instantiates the initializer. This
// class is templated over the fixed and moving images type as well as the
// transform type. An initializer is then constructed by calling the
// \code{New()} method and assigning the result to a
// \doxygen{SmartPointer}.
//
// \index{itk::Euler2DTransform!Instantiation}
// \index{itk::Euler2DTransform!New()}
// \index{itk::Euler2DTransform!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(transform);
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{CenteredTransformInitializer!MomentsOn()}
// \index{CenteredTransformInitializer!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 remaining parameters of the transform are initialized as before.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
transform->SetAngle(0.0);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now the initialized transform object will be set to the registration
// method, and the starting point of the registration is defined by its
// initial parameters.
//
// If the \code{InPlaceOn()} method is called, this initialized transform
// will be the output transform object or ``grafted'' to the output.
// Otherwise, this
// ``InitialTransform'' will be deep-copied or
// ``cloned'' to the output.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransform(transform);
registration->InPlaceOn();
// Software Guide : EndCodeSnippet
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
const double translationScale = 1.0 / 1000.0;
optimizerScales[0] = 1.0;
optimizerScales[1] = translationScale;
optimizerScales[2] = translationScale;
optimizer->SetScales(optimizerScales);
optimizer->SetLearningRate(0.1);
optimizer->SetMinimumStepLength(0.001);
optimizer->SetNumberOfIterations(200);
// 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;
}
// Software Guide : BeginLatex
//
// Since the registration filter has \code{InPlace} set, the transform
// object is grafted to the output and is updated by the registration
// method.
//
// Software Guide : EndLatex
TransformType::ParametersType finalParameters = transform->GetParameters();
const double finalAngle = finalParameters[0];
const double finalTranslationX = finalParameters[1];
const double finalTranslationY = finalParameters[2];
const double rotationCenterX =
registration->GetOutput()->Get()->GetFixedParameters()[0];
const double rotationCenterY =
registration->GetOutput()->Get()->GetFixedParameters()[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 some of the images provided in
// \code{Examples/Data}, for example:
//
// \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 around the geometric center and shifting
// it $13mm$ in $X$ and $17mm$ in $Y$. Both images have
// unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration5}. The registration takes
// $21$ iterations and produces:
//
// \begin{center}
// \begin{verbatim}
// [ 0.174527, 12.4528, 16.0766]
// \end{verbatim}
// \end{center}
//
// These parameters are interpreted as
//
// \begin{itemize}
// \item Angle = $0.174527$ radians
// \item Translation = $( 12.4528, 16.0766 )$ millimeters
// \end{itemize}
//
// Note that the reported translation is not the translation of $(13,17)$
// that might be expected. The reason is that we used the center of
// mass $( 111.204, 131.591 )$ for the fixed center, while the input was
// rotated about the geometric center $( 110.5, 128.5 )$. It is more
// illustrative in this case to take a look at the actual rotation matrix
// and offset resulting from the five parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
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
//
// Which produces the following output.
//
// \begin{verbatim}
// Matrix =
// 0.984809 -0.173642
// 0.173642 0.984809
//
// Offset =
// [36.9919, -1.23402]
// \end{verbatim}
//
// This output illustrates how counter-intuitive the mix of center of
// rotation and translations can be. Figure
// \ref{fig:TranslationAndRotationCenter} will clarify this situation. The
// figure shows the original image on the left. A rotation of $10^{\circ}$
// around the center of the image is shown in the middle. The same rotation
// performed around the origin of coordinates is shown on the right. It can
// be seen here that changing the center of rotation introduces additional
// translations.
//
// Let's analyze what happens to the center of the image that we just
// registered. Under the point of view of rotating $10^{\circ}$ around the
// center and then applying a translation of $(13mm,17mm)$. The image has
// a size of $(221 \times 257)$ pixels and unit spacing. Hence its center
// has coordinates $(110.5,128.5)$. Since the rotation is done around this
// point, the center behaves as the fixed point of the transformation and
// remains unchanged. Then with the $(13mm,17mm)$ translation it is mapped
// to $(123.5,145.5)$ which becomes its final position.
//
// The matrix and offset that we obtained at the end of the registration
// indicate that this should be equivalent to a rotation of $10^{\circ}$
// around the origin, followed by a translation of $(36.99, -1.23)$. Let's
// compute this in detail. First the rotation of the image center by
// $10^{\circ}$ around the origin will move the point
// $(110.5,128.5)$ to $(86.51,145.74)$. Now, applying a translation
// of $(36.99,-1.23)$ maps this point to $(123.50, 144.50)$, which
// is very close to the result of our previous computation.
//
// It is unlikely that we could have chosen these translations as the
// initial guess, since we tend to think about images in a coordinate
// system whose origin is in the center of the image.
//
// \begin{figure}
// \center
// \includegraphics[width=\textwidth]{TranslationAndRotationCenter}
// \itkcaption[Effect of changing the center of rotation]{Effect of changing
// the center of rotation.}
// \label{fig:TranslationAndRotationCenter}
// \end{figure}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// This underscores the importance of using good initialization for
// the center for a transform fixed parameter. By using either the
// center of geometry or center of mass for initialization the
// rotation and translation parameters may have a more intuitive
// interpretation than if only the optimization parameters of
// translation and rotation are initialized.
//
// 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
// images provided as input to the registration method using
// CenteredTransformInitializer.}
// \label{fig:FixedMovingImageRegistration6}
// \end{figure}
//
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration6Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration6DifferenceBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration6DifferenceAfter}
// \itkcaption[CenteredTransformInitializer output images]{Resampled moving
// image (left). Differences between fixed and moving images, before
// registration (center) and after registration (right) with the
// CenteredTransformInitializer.}
// \label{fig:ImageRegistration6Outputs}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration6Outputs} shows the output of the
// registration. The image on the right of this figure shows the differences
// between the fixed image and the resampled moving image after
// registration.
//
// \begin{figure}
// \center
// \includegraphics[height=0.32\textwidth]{ImageRegistration6TraceMetric}
// \includegraphics[height=0.32\textwidth]{ImageRegistration6TraceAngle}
// \includegraphics[height=0.32\textwidth]{ImageRegistration6TraceTranslations}
// \itkcaption[CenteredTransformInitializer output plots]{Plots of the
// Metric, rotation angle, center of rotation and translations during the
// registration using CenteredTransformInitializer.}
// \label{fig:ImageRegistration6Plots}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration6Plots} plots the output parameters of
// the registration process. It includes the metric values at every
// iteration, the angle values at every iteration, and the values of the
// translation components as the registration progresses. Note that this is
// the complementary translation as used in the transform, not the actual
// total translation that is used in the transform offset. We could modify
// the observer to print the total offset instead of printing the array of
// parameters. Let's call that an exercise for the reader!
//
// Software Guide : EndLatex
using ResampleFilterType =
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform(transform);
resample->SetInput(movingImageReader->GetOutput());
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
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());
writer->Update();
// Now compute the difference between the images
// before and after registration.
//
using DifferenceImageType = itk::Image<float, Dimension>;
using DifferenceFilterType = itk::
SubtractImageFilter<FixedImageType, FixedImageType, DifferenceImageType>;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using RescalerType =
RescalerType::Pointer intensityRescaler = RescalerType::New();
intensityRescaler->SetOutputMinimum(0);
intensityRescaler->SetOutputMaximum(255);
difference->SetInput1(fixedImageReader->GetOutput());
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 > 5)
{
writer2->SetFileName(argv[5]);
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 > 4)
{
writer2->SetFileName(argv[4]);
writer2->Update();
}
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << "Error while writing difference images" << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itkEuler2DTransform.h
itkRegularStepGradientDescentOptimizerv4.h
itkCenteredTransformInitializer.h
itkImageFileReader.h
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::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:43
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::ImageRegistrationMethodv4
Interface method for the current registration framework.
Definition: itkImageRegistrationMethodv4.h:117
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::CenteredTransformInitializer
CenteredTransformInitializer is a helper class intended to initialize the center of rotation and the ...
Definition: itkCenteredTransformInitializer.h:61