ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/DeformableRegistration7.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.
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*=========================================================================*/
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{BSplineTransform}
// class for performing registration of two $3D$ images. The example code is
// for the most part identical to the code presented in
// Section~\ref{sec:BSplinesMultiGridImageRegistration}. The major difference
// is that in this example we set the image dimension to 3 and replace the
// \doxygen{LBFGSOptimizerv4} optimizer with the \doxygen{LBFGSBOptimizerv4}.
// We made the modification because we found that LBFGS does not behave well
// when the starting position is at or close to optimal; instead we used
// LBFGSB in unconstrained mode.
//
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGSBOptimizerv4}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The following are the most relevant headers to this example.
//
// \index{itk::BSplineTransform!header}
// \index{itk::LBFGSBOptimizerv4!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The parameter space of the \code{BSplineTransform} is composed by
// the set of all the deformations associated with the nodes of the BSpline
// grid. This large number of parameters enables it to represent a wide
// variety of deformations, at the cost of requiring a
// significant amount of computation time.
//
// \index{itk::BSplineTransform!header}
//
// Software Guide : EndLatex
// The following section of code implements a Command observer
// used to 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 OptimizerType = itk::LBFGSBOptimizerv4;
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->GetCurrentMetricValue() << " ";
std::cout << optimizer->GetInfinityNormOfProjectedGradient() << 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 outputImagefile ";
std::cerr << " [differenceOutputfile] [differenceBeforeRegistration] ";
std::cerr << " [deformationField] ";
return EXIT_FAILURE;
}
constexpr unsigned int ImageDimension = 3;
using PixelType = float;
using FixedImageType = itk::Image<PixelType, ImageDimension>;
using MovingImageType = itk::Image<PixelType, ImageDimension>;
// Software Guide : BeginLatex
//
// We instantiate now the type of the \code{BSplineTransform} using
// as template parameters the type for coordinates representation, the
// dimension of the space, and the order of the BSpline.
//
// \index{BSplineTransform!New}
// \index{BSplineTransform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int SpaceDimension = ImageDimension;
constexpr unsigned int SplineOrder = 3;
using CoordinateRepType = double;
using TransformType =
// Software Guide : EndCodeSnippet
using OptimizerType = itk::LBFGSBOptimizerv4;
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);
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]);
FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
registration->SetFixedImage(fixedImage);
registration->SetMovingImage(movingImageReader->GetOutput());
fixedImageReader->Update();
// Software Guide : BeginLatex
//
// The transform object is constructed, initialized like previous examples
// and passed to the registration method.
//
// \index{itk::ImageRegistrationMethodv4!SetInitialTransform()}
// \index{itk::ImageRegistrationMethodv4!InPlaceOn()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer outputBSplineTransform = TransformType::New();
// Software Guide : EndCodeSnippet
// Initialize the transform
using InitializerType =
InitializerType::Pointer transformInitializer = InitializerType::New();
unsigned int numberOfGridNodesInOneDimension = 8;
TransformType::MeshSizeType meshSize;
meshSize.Fill(numberOfGridNodesInOneDimension - SplineOrder);
transformInitializer->SetTransform(outputBSplineTransform);
transformInitializer->SetImage(fixedImage);
transformInitializer->SetTransformDomainMeshSize(meshSize);
transformInitializer->InitializeTransform();
// Set transform to identity
using ParametersType = TransformType::ParametersType;
const unsigned int numberOfParameters =
outputBSplineTransform->GetNumberOfParameters();
ParametersType parameters(numberOfParameters);
parameters.Fill(0.0);
outputBSplineTransform->SetParameters(parameters);
// Software Guide : BeginCodeSnippet
registration->SetInitialTransform(outputBSplineTransform);
registration->InPlaceOn();
// Software Guide : EndCodeSnippet
// A single level registration process is run using
// the shrink factor 1 and smoothing sigma 0.
//
constexpr unsigned int numberOfLevels = 1;
RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
shrinkFactorsPerLevel.SetSize(numberOfLevels);
shrinkFactorsPerLevel[0] = 1;
RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize(numberOfLevels);
smoothingSigmasPerLevel[0] = 0;
registration->SetNumberOfLevels(numberOfLevels);
registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
registration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);
// Software Guide : BeginLatex
//
// Next we set the parameters of the LBFGSB Optimizer. Note that
// this optimizer does not support scales estimator and sets all
// the parameters scales to one.
// Also, we should set the boundary condition for each variable, where
// \code{boundSelect[i]} can be set as: \code{UNBOUNDED},
// \code{LOWERBOUNDED}, \code{BOTHBOUNDED}, \code{UPPERBOUNDED}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int numParameters =
outputBSplineTransform->GetNumberOfParameters();
OptimizerType::BoundSelectionType boundSelect(numParameters);
OptimizerType::BoundValueType upperBound(numParameters);
OptimizerType::BoundValueType lowerBound(numParameters);
boundSelect.Fill(OptimizerType::UNBOUNDED);
upperBound.Fill(0.0);
lowerBound.Fill(0.0);
optimizer->SetBoundSelection(boundSelect);
optimizer->SetUpperBound(upperBound);
optimizer->SetLowerBound(lowerBound);
optimizer->SetCostFunctionConvergenceFactor(1e+12);
optimizer->SetGradientConvergenceTolerance(1.0e-35);
optimizer->SetNumberOfIterations(500);
optimizer->SetMaximumNumberOfFunctionEvaluations(500);
optimizer->SetMaximumNumberOfCorrections(5);
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
std::cout << "Starting Registration " << std::endl;
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;
}
OptimizerType::ParametersType finalParameters =
outputBSplineTransform->GetParameters();
std::cout << "Last Transform Parameters" << std::endl;
std::cout << finalParameters << std::endl;
// Finally we use the last transform in order to resample the image.
//
using ResampleFilterType =
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform(outputBSplineTransform);
resample->SetInput(movingImageReader->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 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 & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
using DifferenceFilterType =
FixedImageType,
OutputImageType>;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
WriterType::Pointer writer2 = WriterType::New();
writer2->SetInput(difference->GetOutput());
// Compute the difference image between the
// fixed and resampled moving image.
if (argc >= 5)
{
difference->SetInput1(fixedImageReader->GetOutput());
difference->SetInput2(resample->GetOutput());
writer2->SetFileName(argv[4]);
try
{
writer2->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
}
// Compute the difference image between the
// fixed and moving image before registration.
if (argc >= 6)
{
writer2->SetFileName(argv[5]);
difference->SetInput1(fixedImageReader->GetOutput());
difference->SetInput2(movingImageReader->GetOutput());
try
{
writer2->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
}
// Generate the explicit deformation field resulting from
// the registration.
using VectorPixelType = itk::Vector<float, ImageDimension>;
using DisplacementFieldImageType =
using DisplacementFieldGeneratorType =
itk::TransformToDisplacementFieldFilter<DisplacementFieldImageType,
CoordinateRepType>;
DisplacementFieldGeneratorType::Pointer dispfieldGenerator =
DisplacementFieldGeneratorType::New();
dispfieldGenerator->UseReferenceImageOn();
dispfieldGenerator->SetReferenceImage(fixedImage);
dispfieldGenerator->SetTransform(outputBSplineTransform);
try
{
dispfieldGenerator->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "Exception detected while generating deformation field";
std::cerr << " : " << err << std::endl;
return EXIT_FAILURE;
}
FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
fieldWriter->SetInput(dispfieldGenerator->GetOutput());
if (argc >= 7)
{
fieldWriter->SetFileName(argv[6]);
try
{
fieldWriter->Update();
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << "Exception thrown " << 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
itk::SquaredDifferenceImageFilter
Implements pixel-wise the computation of squared difference.
Definition: itkSquaredDifferenceImageFilter.h:88
itkTransformToDisplacementFieldFilter.h
itk::LBFGSBOptimizerv4
Limited memory Broyden Fletcher Goldfarb Shannon minimization with simple bounds.
Definition: itkLBFGSBOptimizerv4.h:67
itk::Vector
A templated class holding a n-Dimensional vector.
Definition: itkVector.h:62
itkImageFileReader.h
itk::SmartPointer< Self >
itkCastImageFilter.h
itk::TransformToDisplacementFieldFilter
Generate a displacement field from a coordinate transform.
Definition: itkTransformToDisplacementFieldFilter.h:55
itkLBFGSBOptimizerv4.h
itk::BSplineTransformInitializer
BSplineTransformInitializer is a helper class intended to initialize the control point grid such that...
Definition: itkBSplineTransformInitializer.h:41
itkImageRegistrationMethodv4.h
itkMeanSquaresImageToImageMetricv4.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itk::BSplineTransform
Deformable transform using a BSpline representation.
Definition: itkBSplineTransform.h:103
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkBSplineTransform.h
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:43
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkIdentityTransform.h
itkImageFileWriter.h
itkSquaredDifferenceImageFilter.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
itkBSplineTransformInitializer.h
itk::Math::e
static constexpr double e
Definition: itkMath.h:54
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
itkCommand.h