ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv4/MultiResImageRegistration2.cxx
/*=========================================================================
*
* Copyright Insight Software Consortium
*
* 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() {};
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 = itk::LinearInterpolateImageFunction<
InternalImageType,
double >;
InternalImageType,
InternalImageType >;
using OptimizerScalesType = OptimizerType::ScalesType;
InternalImageType,
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 = itk::CastImageFilter<
FixedImageType, InternalImageType >;
using MovingCastFilterType = itk::CastImageFilter<
MovingImageType, InternalImageType >;
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 = itk::CenteredTransformInitializer<
TransformType, 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( 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 = itk::ResampleImageFilter<
MovingImageType,
FixedImageType >;
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 CastFilterType = itk::CastImageFilter<
FixedImageType,
OutputImageType >;
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;
}