ITK
6.0.0
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
*
* https://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 : 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
#include "
itkImageRegistrationMethodv4.h
"
#include "
itkMeanSquaresImageToImageMetricv4.h
"
// 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
#include "
itkBSplineTransform.h
"
#include "
itkLBFGSBOptimizerv4.h
"
// 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
#include "
itkImageFileReader.h
"
#include "
itkImageFileWriter.h
"
#include "
itkResampleImageFilter.h
"
#include "
itkCastImageFilter.h
"
#include "
itkSquaredDifferenceImageFilter.h
"
#include "
itkIdentityTransform.h
"
#include "
itkBSplineTransformInitializer.h
"
#include "
itkTransformToDisplacementFieldFilter.h
"
// 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 =
itk::BSplineTransform<CoordinateRepType, SpaceDimension, SplineOrder>
;
// Software Guide : EndCodeSnippet
using
OptimizerType =
itk::LBFGSBOptimizerv4
;
using
MetricType =
itk::MeanSquaresImageToImageMetricv4<FixedImageType, MovingImageType>
;
using
RegistrationType =
itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType>
;
auto
metric =
MetricType::New
();
auto
optimizer =
OptimizerType::New
();
auto
registration =
RegistrationType::New
();
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
using
FixedImageReaderType =
itk::ImageFileReader<FixedImageType>
;
using
MovingImageReaderType =
itk::ImageFileReader<MovingImageType>
;
auto
fixedImageReader =
FixedImageReaderType::New
();
auto
movingImageReader =
MovingImageReaderType::New
();
fixedImageReader->SetFileName(argv[1]);
movingImageReader->SetFileName(argv[2]);
const
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
auto
outputBSplineTransform =
TransformType::New
();
// Software Guide : EndCodeSnippet
// Initialize the transform
using
InitializerType =
itk::BSplineTransformInitializer<TransformType, FixedImageType>
;
auto
transformInitializer =
InitializerType::New
();
const
unsigned
int
numberOfGridNodesInOneDimension = 8;
auto
meshSize = itk::MakeFilled<TransformType::MeshSizeType>(
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(1
e
+12);
optimizer->SetGradientConvergenceTolerance(1.0
e
-35);
optimizer->SetNumberOfIterations(500);
optimizer->SetMaximumNumberOfFunctionEvaluations(500);
optimizer->SetMaximumNumberOfCorrections(5);
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
auto
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;
}
const
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 =
itk::ResampleImageFilter<MovingImageType, FixedImageType>
;
auto
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
OutputImageType =
itk::Image<OutputPixelType, ImageDimension>
;
using
CastFilterType =
itk::CastImageFilter<FixedImageType, OutputImageType>
;
using
WriterType =
itk::ImageFileWriter<OutputImageType>
;
auto
writer =
WriterType::New
();
auto
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 =
itk::SquaredDifferenceImageFilter
<FixedImageType,
FixedImageType,
OutputImageType>;
auto
difference =
DifferenceFilterType::New
();
auto
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 =
itk::Image<VectorPixelType, ImageDimension>
;
using
DisplacementFieldGeneratorType =
itk::TransformToDisplacementFieldFilter
<DisplacementFieldImageType,
CoordinateRepType>;
auto
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;
}
using
FieldWriterType =
itk::ImageFileWriter<DisplacementFieldImageType>
;
auto
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;
}
Pointer
SmartPointer< Self > Pointer
Definition:
itkAddImageFilter.h:93
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition:
itkCastImageFilter.h:100
itk::SquaredDifferenceImageFilter
Implements pixel-wise the computation of squared difference.
Definition:
itkSquaredDifferenceImageFilter.h:82
ConstPointer
SmartPointer< const Self > ConstPointer
Definition:
itkAddImageFilter.h:94
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:90
itkBSplineTransform.h
itk::Command
class ITK_FORWARD_EXPORT Command
Definition:
itkObject.h:42
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkIdentityTransform.h
itkImageFileWriter.h
itk::ExceptionObject
Standard exception handling object.
Definition:
itkExceptionObject.h:50
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:61
itkBSplineTransformInitializer.h
itk::Math::e
static constexpr double e
Definition:
itkMath.h:56
itk::Image
Templated n-dimensional image class.
Definition:
itkImage.h:88
itk::EventObject
Abstraction of the Events used to communicating among filters and with GUIs.
Definition:
itkEventObject.h:58
New
static Pointer New()
AddImageFilter
Definition:
itkAddImageFilter.h:81
itkResampleImageFilter.h
itkCommand.h
Superclass
BinaryGeneratorImageFilter< TInputImage1, TInputImage2, TOutputImage > Superclass
Definition:
itkAddImageFilter.h:90
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