ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/MultiResImageRegistration2.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: {BrainT1SliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceShifted13x17y.png}
// OUTPUTS: {MultiResImageRegistration2Output.png}
// ARGUMENTS: 100
// OUTPUTS: {MultiResImageRegistration2CheckerboardBefore.png}
// OUTPUTS: {MultiResImageRegistration2CheckerboardAfter.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of more complex components of the
// registration framework. In particular, it introduces the use of the
// \doxygen{AffineTransform} and the importance of fine-tuning the scale
// parameters of the optimizer.
//
// \index{itk::ImageRegistrationMethod!AffineTransform}
// \index{itk::ImageRegistrationMethod!Scaling parameter space}
// \index{itk::AffineTransform!Image Registration}
//
// The AffineTransform is a linear transformation that maps lines into
// lines. It can be used to represent translations, rotations, anisotropic
// scaling, shearing or any combination of them. Details about the affine
// transform can be seen in Section~\ref{sec:AffineTransform}.
//
// In order to use the AffineTransform class, the following header
// must be included.
//
// \index{itk::AffineTransform!Header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
#include "itkImage.h"
// The following section of code implements an 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() << " "
<< m_CumulativeIterationIndex++ << std::endl;
}
private:
unsigned int m_CumulativeIterationIndex{ 0 };
};
// The following section of code implements a Command observer
// that will control the modification of optimizer parameters
// at every change of resolution level.
//
template <typename TRegistration>
class RegistrationInterfaceCommand : public itk::Command
{
public:
using Self = RegistrationInterfaceCommand;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
RegistrationInterfaceCommand() = default;
public:
using RegistrationType = TRegistration;
using RegistrationPointer = RegistrationType *;
using OptimizerPointer = OptimizerType *;
void
Execute(itk::Object * object, const itk::EventObject & event) override
{
if (!(itk::IterationEvent().CheckEvent(&event)))
{
return;
}
auto registration = static_cast<RegistrationPointer>(object);
auto optimizer =
static_cast<OptimizerPointer>(registration->GetModifiableOptimizer());
std::cout << "-------------------------------------" << std::endl;
std::cout << "MultiResolution Level : " << registration->GetCurrentLevel()
<< std::endl;
std::cout << std::endl;
if (registration->GetCurrentLevel() == 0)
{
optimizer->SetMaximumStepLength(16.00);
optimizer->SetMinimumStepLength(0.01);
}
else
{
optimizer->SetMaximumStepLength(optimizer->GetMaximumStepLength() /
4.0);
optimizer->SetMinimumStepLength(optimizer->GetMinimumStepLength() /
10.0);
}
}
void
Execute(const itk::Object *, const itk::EventObject &) override
{
return;
}
};
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 [backgroundGrayLevel]";
std::cerr << " [checkerboardbefore] [CheckerBoardAfter]";
std::cerr << " [useExplicitPDFderivatives ] " << std::endl;
std::cerr << " [numberOfBins] [numberOfSamples ] " << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int Dimension = 2;
using PixelType = unsigned short;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
using InternalPixelType = float;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
// Software Guide : BeginLatex
//
// The configuration of the registration method in this example closely
// follows the procedure in the previous section. The main changes involve
// the construction and initialization of the transform. The instantiation
// of the transform type requires only the dimension of the space and the
// type used for representing space coordinates.
//
// \index{itk::AffineTransform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
using InterpolatorType =
using MetricType =
InternalImageType>;
using OptimizerScalesType = OptimizerType::ScalesType;
using RegistrationType =
InternalImageType>;
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
MetricType::Pointer metric = MetricType::New();
registration->SetOptimizer(optimizer);
registration->SetInterpolator(interpolator);
registration->SetMetric(metric);
// Software Guide : BeginLatex
//
// The transform is constructed using the standard \code{New()} method and
// assigning it to a SmartPointer.
//
// \index{itk::AffineTransform!New()}
// \index{itk::AffineTransform!Pointer}
// \index{itk::Multi\-Resolution\-Image\-Registration\-Method!SetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
registration->SetTransform(transform);
// 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]);
using FixedCastFilterType =
using MovingCastFilterType =
FixedCastFilterType::Pointer fixedCaster = FixedCastFilterType::New();
MovingCastFilterType::Pointer movingCaster = MovingCastFilterType::New();
fixedCaster->SetInput(fixedImageReader->GetOutput());
movingCaster->SetInput(movingImageReader->GetOutput());
registration->SetFixedImage(fixedCaster->GetOutput());
registration->SetMovingImage(movingCaster->GetOutput());
fixedCaster->Update();
registration->SetFixedImageRegion(
fixedCaster->GetOutput()->GetBufferedRegion());
// Software Guide : BeginLatex
//
// One of the easiest ways of preparing a consistent set of parameters for
// the transform is to use the \doxygen{CenteredTransformInitializer}. Once
// the transform is initialized, we can invoke its \code{GetParameters()}
// method to extract the array of parameters. Finally the array is passed
// to the registration method using its
// \code{SetInitialTransformParameters()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using TransformInitializerType =
FixedImageType,
MovingImageType>;
TransformInitializerType::Pointer initializer =
TransformInitializerType::New();
initializer->SetTransform(transform);
initializer->SetFixedImage(fixedImageReader->GetOutput());
initializer->SetMovingImage(movingImageReader->GetOutput());
initializer->MomentsOn();
initializer->InitializeTransform();
registration->SetInitialTransformParameters(transform->GetParameters());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The set of parameters in the AffineTransform have different
// dynamic ranges. Typically the parameters associated with the matrix
// have values around $[-1:1]$, although they are not restricted to this
// interval. Parameters associated with translations, on the other hand,
// tend to have much higher values, typically in the order of $10.0$ to
// $100.0$. This difference in dynamic range negatively affects the
// performance of gradient descent optimizers. ITK provides a mechanism to
// compensate for such differences in values among the parameters when
// they are passed to the optimizer. The mechanism consists of providing an
// array of scale factors to the optimizer. These factors re-normalize the
// gradient components before they are used to compute the step of the
// optimizer at the current iteration. In our particular case, a common
// choice for the scale parameters is to set to $1.0$ all those associated
// with the matrix coefficients, that is, the first $N \times N$
// factors. Then, we set the remaining scale factors to a small value. The
// following code sets up the scale coefficients.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
optimizerScales[0] = 1.0; // scale for M11
optimizerScales[1] = 1.0; // scale for M12
optimizerScales[2] = 1.0; // scale for M21
optimizerScales[3] = 1.0; // scale for M22
optimizerScales[4] = 1.0 / 1e7; // scale for translation on X
optimizerScales[5] = 1.0 / 1e7; // scale for translation on Y
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Here the affine transform is represented by the matrix $\bf{M}$ and the
// vector $\bf{T}$. The transformation of a point $\bf{P}$ into $\bf{P'}$
// is expressed as
//
// \begin{equation}
// \left[
// \begin{array}{c}
// {P'}_x \\ {P'}_y \\ \end{array}
// \right]
// =
// \left[
// \begin{array}{cc}
// M_{11} & M_{12} \\ M_{21} & M_{22} \\ \end{array}
// \right]
// \cdot
// \left[
// \begin{array}{c}
// P_x \\ P_y \\ \end{array}
// \right]
// +
// \left[
// \begin{array}{c}
// T_x \\ T_y \\ \end{array}
// \right]
// \end{equation}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The array of scales is then passed to the optimizer using the
// \code{SetScales()} method.
//
// \index{itk::Optimizer!SetScales()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetScales(optimizerScales);
// Software Guide : EndCodeSnippet
metric->SetNumberOfHistogramBins(128);
metric->SetNumberOfSpatialSamples(50000);
if (argc > 8)
{
// optionally, override the values with numbers taken from the command
// line arguments.
metric->SetNumberOfHistogramBins(std::stoi(argv[8]));
}
if (argc > 9)
{
// optionally, override the values with numbers taken from the command
// line arguments.
metric->SetNumberOfSpatialSamples(std::stoi(argv[9]));
}
// Software Guide : BeginLatex
//
// Given that the Mattes Mutual Information metric uses a random iterator
// in order to collect the samples from the images, it is usually
// convenient to initialize the seed of the random number generator.
//
// \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!ReinitializeSeed()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
metric->ReinitializeSeed(76926294);
// Software Guide : EndCodeSnippet
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]));
}
// Software Guide : BeginLatex
//
// The step length has to be proportional to the expected values of the
// parameters in the search space. Since the expected values of the matrix
// coefficients are around $1.0$, the initial step of the optimization
// should be a small number compared to $1.0$. As a guideline, it is
// useful to think of the matrix coefficients as combinations of
// $cos(\theta)$ and $sin(\theta)$. This leads to use values close to the
// expected rotation measured in radians. For example, a rotation of $1.0$
// degree is about $0.017$ radians. As in the previous example, the
// maximum and minimum step length of the optimizer are set by the
// \code{RegistrationInterfaceCommand} when it is called at the beginning
// of registration at each multi-resolution level.
//
// Software Guide : EndLatex
optimizer->SetNumberOfIterations(200);
optimizer->SetRelaxationFactor(0.8);
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
// Create the Command interface observer and register it with the optimizer.
//
using CommandType = RegistrationInterfaceCommand<RegistrationType>;
CommandType::Pointer command = CommandType::New();
registration->AddObserver(itk::IterationEvent(), command);
registration->SetNumberOfLevels(3);
try
{
registration->Update();
std::cout << "Optimizer stop condition: "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch (const itk::ExceptionObject & err)
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return EXIT_FAILURE;
}
std::cout << "Optimizer Stopping Condition = "
<< optimizer->GetStopCondition() << std::endl;
using ParametersType = RegistrationType::ParametersType;
ParametersType finalParameters = registration->GetLastTransformParameters();
double TranslationAlongX = finalParameters[4];
double TranslationAlongY = finalParameters[5];
unsigned int numberOfIterations = optimizer->GetCurrentIteration();
double bestValue = optimizer->GetValue();
// Print out results
//
std::cout << "Result = " << std::endl;
std::cout << " Translation X = " << TranslationAlongX << std::endl;
std::cout << " Translation Y = " << TranslationAlongY << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example using the same multi-modality images as
// before. The registration converges after $5$ iterations in the first
// level, $7$ in the second level and $4$ in the third level. The final
// results when printed as an array of parameters are
//
// \begin{verbatim}
// [1.00164, 0.00147688, 0.00168372, 1.0027, 12.6296, 16.4768]
// \end{verbatim}
//
// By reordering them as coefficient of matrix $\bf{M}$ and vector $\bf{T}$
// they can now be seen as
//
// \begin{equation}
// M =
// \left[
// \begin{array}{cc}
// 1.00164 & 0.0014 \\ 0.00168 & 1.0027 \\ \end{array}
// \right]
// \mbox{ and }
// T =
// \left[
// \begin{array}{c}
// 12.6296 \\ 16.4768 \\ \end{array}
// \right]
// \end{equation}
//
// In this form, it is easier to interpret the effect of the
// transform. The matrix $\bf{M}$ is responsible for scaling, rotation and
// shearing while $\bf{T}$ is responsible for translations. It can be seen
// that the translation values in this case closely match the true
// misalignment introduced in the moving image.
//
// It is important to note that once the images are registered at a
// sub-pixel level, any further improvement of the registration relies
// heavily on the quality of the interpolator. It may then be reasonable to
// use a coarse and fast interpolator in the lower resolution levels and
// switch to a high-quality but slow interpolator in the final resolution
// level.
//
// Software Guide : EndLatex
using ResampleFilterType =
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters(finalParameters);
finalTransform->SetFixedParameters(transform->GetFixedParameters());
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform(finalTransform);
resample->SetInput(movingImageReader->GetOutput());
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
PixelType backgroundGrayLevel = 100;
if (argc > 4)
{
backgroundGrayLevel = std::stoi(argv[4]);
}
resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
resample->SetOutputOrigin(fixedImage->GetOrigin());
resample->SetOutputSpacing(fixedImage->GetSpacing());
resample->SetOutputDirection(fixedImage->GetDirection());
resample->SetDefaultPixelValue(backgroundGrayLevel);
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();
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2Output}
// \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2CheckerboardBefore}
// \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2CheckerboardAfter}
// \itkcaption[Multi-Resolution Registration Input Images]{Mapped moving
// image (left) and composition of fixed and moving images before (center)
// and after (right) multi-resolution registration with the AffineTransform
// class.} \label{fig:MultiResImageRegistration2Output} \end{figure}
//
// The result of resampling the moving image is shown in the left image
// of Figure \ref{fig:MultiResImageRegistration2Output}. The center and
// right images of the figure present a checkerboard composite of the fixed
// and moving images before and after registration.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[height=0.44\textwidth]{MultiResImageRegistration2TraceTranslations}
// \includegraphics[height=0.44\textwidth]{MultiResImageRegistration2TraceMetric}
// \itkcaption[Multi-Resolution Registration output plots]{Sequence of
// translations and metric values at each iteration of the optimizer for
// multi-resolution with the AffineTransform class.}
// \label{fig:MultiResImageRegistration2Trace}
// \end{figure}
//
// Figure \ref{fig:MultiResImageRegistration2Trace} (left) presents the
// sequence of translations followed by the optimizer as it searched the
// parameter space. The right side of the same figure shows the sequence of
// metric values computed as the optimizer explored the parameter space.
//
// Software Guide : EndLatex
//
// Generate checkerboards before and after registration
//
using CheckerBoardFilterType = itk::CheckerBoardImageFilter<FixedImageType>;
CheckerBoardFilterType::Pointer checker = CheckerBoardFilterType::New();
checker->SetInput1(fixedImage);
checker->SetInput2(resample->GetOutput());
caster->SetInput(checker->GetOutput());
writer->SetInput(caster->GetOutput());
resample->SetDefaultPixelValue(0);
// Write out checkerboard outputs
// Before registration
TransformType::Pointer identityTransform = TransformType::New();
identityTransform->SetIdentity();
resample->SetTransform(identityTransform);
if (argc > 5)
{
writer->SetFileName(argv[5]);
writer->Update();
}
// After registration
resample->SetTransform(finalTransform);
if (argc > 6)
{
writer->SetFileName(argv[6]);
writer->Update();
}
return EXIT_SUCCESS;
}
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itkMultiResolutionImageRegistrationMethod.h
itkRegularStepGradientDescentOptimizer.h
itk::MultiResolutionImageRegistrationMethod
Base class for multi-resolution image registration methods.
Definition: itkMultiResolutionImageRegistrationMethod.h:72
itkCenteredTransformInitializer.h
itkImageFileReader.h
itk::CheckerBoardImageFilter
Combines two images in a checkerboard pattern.
Definition: itkCheckerBoardImageFilter.h:46
itkImage.h
itk::SmartPointer< Self >
itkCastImageFilter.h
itkAffineTransform.h
itk::AffineTransform
Definition: itkAffineTransform.h:101
itk::RegularStepGradientDescentOptimizer
Implement a gradient descent optimizer.
Definition: itkRegularStepGradientDescentOptimizer.h:33
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::LinearInterpolateImageFunction
Linearly interpolate an image at specified positions.
Definition: itkLinearInterpolateImageFunction.h:50
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itkCheckerBoardImageFilter.h
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:43
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkImageFileWriter.h
itkRecursiveMultiResolutionPyramidImageFilter.h
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
itkResampleImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itkCommand.h
itkMattesMutualInformationImageToImageMetric.h
itk::CenteredTransformInitializer
CenteredTransformInitializer is a helper class intended to initialize the center of rotation and the ...
Definition: itkCenteredTransformInitializer.h:61
itk::MattesMutualInformationImageToImageMetric
Computes the mutual information between two images to be registered using the method of Mattes et al.
Definition: itkMattesMutualInformationImageToImageMetric.h:116