ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/DeformableRegistration4.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 $2D$ images in an ITKv4
// registration framework. Due to the large number of parameters of
// the BSpline transform, we will use a \doxygen{LBFGSOptimizerv4}
// instead of a simple steepest descent or a conjugate gradient
// descent optimizer.
//
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGSOptimizerv4}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The following are the most relevant headers to this example.
//
// \index{itk::BSplineTransform!header}
// \index{itk::LBFGSOptimizerv4!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 makes it possible 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
// NOTE: the LBFGSOptimizerv4 does not invoke events
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 = 2;
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::LBFGSOptimizerv4;
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]);
fixedImageReader->Update();
FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
// Software Guide : BeginLatex
//
// The transform object is constructed below.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Fixed parameters of the BSpline transform should be defined
// before the registration. These parameters define origin,
// dimension, direction and mesh size of the transform grid
// and are set based on specifications of the fixed image space
// lattice. We can use \doxygen{BSplineTransformInitializer} to
// initialize fixed parameters of a BSpline transform.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InitializerType =
InitializerType::Pointer transformInitializer = InitializerType::New();
unsigned int numberOfGridNodesInOneDimension = 8;
TransformType::MeshSizeType meshSize;
meshSize.Fill(numberOfGridNodesInOneDimension - SplineOrder);
transformInitializer->SetTransform(transform);
transformInitializer->SetImage(fixedImage);
transformInitializer->SetTransformDomainMeshSize(meshSize);
transformInitializer->InitializeTransform();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// After setting the fixed parameters of the transform, we set the
// initial transform to be an identity transform. It is like setting
// all the transform parameters to zero in created parameter space.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
transform->SetIdentity();
// Software Guide : EndCodeSnippet
std::cout << "Initial Parameters = " << std::endl;
std::cout << transform->GetParameters() << std::endl;
// Software Guide : BeginLatex
//
// Then, the initialized transform is connected to the registration
// object and is set to be optimized directly during the registration
// process.
//
// Calling \code{InPlaceOn()} means that the current initialized transform
// will optimized directly and is grafted to the output, so it can be
// considered as the output transform object. Otherwise, the initial
// transform will be copied or ``cloned'' to the output transform object,
// and the copied object will be optimized during the registration process.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransform(transform);
registration->InPlaceOn();
// Software Guide : EndCodeSnippet
registration->SetFixedImage(fixedImage);
registration->SetMovingImage(movingImageReader->GetOutput());
// Software Guide : BeginLatex
//
// The \doxygen{RegistrationParameterScalesFromPhysicalShift} class
// is used to estimate the parameters scales before we set the optimizer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using ScalesEstimatorType =
ScalesEstimatorType::Pointer scalesEstimator = ScalesEstimatorType::New();
scalesEstimator->SetMetric(metric);
scalesEstimator->SetTransformForward(true);
scalesEstimator->SetSmallParameterVariation(1.0);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now the scale estimator is passed to the \doxygen{LBFGSOptimizerv4},
// and we set other parameters of the optimizer as well.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetGradientConvergenceTolerance(5e-2);
optimizer->SetLineSearchAccuracy(1.2);
optimizer->SetDefaultStepLength(1.5);
optimizer->TraceOn();
optimizer->SetMaximumNumberOfFunctionEvaluations(1000);
optimizer->SetScalesEstimator(scalesEstimator);
// 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(1);
shrinkFactorsPerLevel[0] = 1;
RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize(1);
smoothingSigmasPerLevel[0] = 0;
registration->SetNumberOfLevels(numberOfLevels);
registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
registration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);
// Add time and memory probes
std::cout << std::endl << "Starting Registration" << std::endl;
try
{
memorymeter.Start("Registration");
chronometer.Start("Registration");
registration->Update();
chronometer.Stop("Registration");
memorymeter.Stop("Registration");
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;
}
// Report the time and memory taken by the registration
chronometer.Report(std::cout);
memorymeter.Report(std::cout);
// Software Guide : BeginLatex
//
// Let's execute this example using the rat lung images from the previous
// examples.
//
// \begin{itemize}
// \item \code{RatLungSlice1.mha}
// \item \code{RatLungSlice2.mha}
// \end{itemize}
//
// The \emph{transform} object is updated during the registration process
// and is passed to the resampler to map the moving image space onto the
// fixed image space.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
OptimizerType::ParametersType finalParameters = transform->GetParameters();
// Software Guide : EndCodeSnippet
std::cout << "Last Transform Parameters" << std::endl;
std::cout << finalParameters << std::endl;
using ResampleFilterType =
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform(transform);
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(transform);
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
itkTimeProbesCollectorBase.h
itk::SquaredDifferenceImageFilter
Implements pixel-wise the computation of squared difference.
Definition: itkSquaredDifferenceImageFilter.h:88
itkTransformToDisplacementFieldFilter.h
itk::ResourceProbesCollectorBase::Start
virtual void Start(const char *name)
itk::MemoryProbesCollectorBase
Aggregates a set of memory probes.
Definition: itkMemoryProbesCollectorBase.h:37
itk::Vector
A templated class holding a n-Dimensional vector.
Definition: itkVector.h:62
itkImageFileReader.h
itk::CorrelationImageToImageMetricv4
Class implementing normalized cross correlation image metric.
Definition: itkCorrelationImageToImageMetricv4.h:78
itk::ResourceProbesCollectorBase::Report
virtual void Report(std::ostream &os=std::cout, bool printSystemInfo=true, bool printReportHead=true, bool useTabs=false)
itkCastImageFilter.h
itk::TransformToDisplacementFieldFilter
Generate a displacement field from a coordinate transform.
Definition: itkTransformToDisplacementFieldFilter.h:55
itk::BSplineTransformInitializer
BSplineTransformInitializer is a helper class intended to initialize the control point grid such that...
Definition: itkBSplineTransformInitializer.h:41
itkImageRegistrationMethodv4.h
itkMemoryProbesCollectorBase.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
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::LBFGSOptimizerv4
Wrap of the vnl_lbfgs algorithm for use in ITKv4 registration framework. The vnl_lbfgs is a wrapper f...
Definition: itkLBFGSOptimizerv4.h:87
itkImageFileWriter.h
itkSquaredDifferenceImageFilter.h
itkCorrelationImageToImageMetricv4.h
itk::ImageRegistrationMethodv4
Interface method for the current registration framework.
Definition: itkImageRegistrationMethodv4.h:117
itk::ResampleImageFilter
Resample an image via a coordinate transform.
Definition: itkResampleImageFilter.h:90
itkBSplineTransformInitializer.h
itk::Math::e
static constexpr double e
Definition: itkMath.h:54
itk::RegistrationParameterScalesFromPhysicalShift
Registration helper class for estimating scales of transform parameters a step sizes,...
Definition: itkRegistrationParameterScalesFromPhysicalShift.h:35
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itkResampleImageFilter.h
itkLBFGSOptimizerv4.h
itk::ResourceProbesCollectorBase::Stop
virtual void Stop(const char *name)
itk::TimeProbesCollectorBase
Aggregates a set of time probes.
Definition: itkTimeProbesCollectorBase.h:38