ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration12.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 : BeginLatex
//
// This example illustrates the use of \code{SpatialObject}s as masks for
// selecting the pixels that should contribute to the computation of Image
// Metrics. This example is almost identical to ImageRegistration6 with the
// exception that the \code{SpatialObject} masks are created and passed to the
// image metric.
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The most important header in this example is the one corresponding to the
// \doxygen{ImageMaskSpatialObject} class.
//
// \index{itk::ImageMaskSpatialObject!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
//
// The following section of code implements a command 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() << std::endl;
}
};
int
main(int argc, char * argv[])
{
if (argc < 5)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile fixedImageMaskFile";
std::cerr << " outputImagefile [differenceOutputfile] ";
std::cerr << " [differenceBeforeRegistration] " << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int Dimension = 2;
using PixelType = float;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
using TransformType = itk::Euler2DTransform<double>;
using MetricType =
using RegistrationType = itk::
ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType>;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
TransformType::Pointer transform = TransformType::New();
registration->SetInitialTransform(transform);
registration->InPlaceOn();
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]);
registration->SetFixedImage(fixedImageReader->GetOutput());
registration->SetMovingImage(movingImageReader->GetOutput());
fixedImageReader->Update();
using TransformInitializerType =
FixedImageType,
MovingImageType>;
TransformInitializerType::Pointer initializer =
TransformInitializerType::New();
initializer->SetTransform(transform);
initializer->SetFixedImage(fixedImageReader->GetOutput());
initializer->SetMovingImage(movingImageReader->GetOutput());
initializer->MomentsOn();
initializer->InitializeTransform();
transform->SetAngle(0.0);
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
const double translationScale = 1.0 / 1000.0;
optimizerScales[0] = 1.0;
optimizerScales[1] = translationScale;
optimizerScales[2] = translationScale;
optimizer->SetScales(optimizerScales);
optimizer->SetLearningRate(0.1);
optimizer->SetMinimumStepLength(0.001);
optimizer->SetNumberOfIterations(200);
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
// Software Guide : BeginLatex
//
// Here we instantiate the type of the \doxygen{ImageMaskSpatialObject}
// using the same dimension of the images to be registered.
//
// \index{itk::ImageMaskSpatialObject!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Then we use the type for creating the spatial object mask that will
// restrict the registration to a reduced region of the image.
//
// \index{itk::ImageMaskSpatialObject!New}
// \index{itk::ImageMaskSpatialObject!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
MaskType::Pointer spatialObjectMask = MaskType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The mask in this case is read from a binary file using the
// \code{ImageFileReader} instantiated for an \code{unsigned char} pixel
// type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using ImageMaskType = itk::Image<unsigned char, Dimension>;
using MaskReaderType = itk::ImageFileReader<ImageMaskType>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The reader is constructed and a filename is passed to it.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
MaskReaderType::Pointer maskReader = MaskReaderType::New();
maskReader->SetFileName(argv[3]);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As usual, the reader is triggered by invoking its \code{Update()}
// method. Since this may eventually throw an exception, the call must be
// placed in a \code{try/catch} block. Note that a full fledged application
// will place this \code{try/catch} block at a much higher level, probably
// under the control of the GUI.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
maskReader->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output of the mask reader is connected as input to the
// \code{ImageMaskSpatialObject}.
//
// \index{itk::ImageMaskSpatialObject!SetImage()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
spatialObjectMask->SetImage(maskReader->GetOutput());
spatialObjectMask->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, the spatial object mask is passed to the image metric.
//
// \index{itk::ImageToImageMetricv4!SetFixedImageMask()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
metric->SetFixedImageMask(spatialObjectMask);
// Software Guide : EndCodeSnippet
// One level registration process without shrinking and smoothing.
//
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);
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 = transform->GetParameters();
const double finalAngle = finalParameters[0];
const double finalTranslationX = finalParameters[1];
const double finalTranslationY = finalParameters[2];
const double rotationCenterX =
registration->GetOutput()->Get()->GetFixedParameters()[0];
const double rotationCenterY =
registration->GetOutput()->Get()->GetFixedParameters()[1];
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
const double bestValue = optimizer->GetValue();
// Print out results
//
const double finalAngleInDegrees = finalAngle * 45.0 / std::atan(1.0);
std::cout << "Result = " << std::endl;
std::cout << " Angle (radians) " << finalAngle << std::endl;
std::cout << " Angle (degrees) " << finalAngleInDegrees << std::endl;
std::cout << " Translation X = " << finalTranslationX << std::endl;
std::cout << " Translation Y = " << finalTranslationY << std::endl;
std::cout << " Fixed Center X = " << rotationCenterX << std::endl;
std::cout << " Fixed Center Y = " << rotationCenterY << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example over some of the images provided in
// \code{Examples/Data}, for example:
//
// \begin{itemize}
// \item \code{BrainProtonDensitySliceBorder20.png}
// \item \code{BrainProtonDensitySliceR10X13Y17.png}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first
// image by $10$ degrees and shifting it $13mm$ in $X$ and $17mm$ in
// $Y$. Both images have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration5}.
//
// The registration converges after $20$ iterations and produces the
// following results:
//
// \begin{verbatim}
//
// Angle (radians) 0.174712
// Angle (degrees) 10.0103
// Translation X = 12.4521
// Translation Y = 16.0765
//
// \end{verbatim}
//
// These values are a very close match to the true misalignments
// introduced in the moving image.
//
// Now we resample the moving image using the transform resulting from the
// registration process.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::MatrixType matrix = transform->GetMatrix();
TransformType::OffsetType offset = transform->GetOffset();
std::cout << "Matrix = " << std::endl << matrix << std::endl;
std::cout << "Offset = " << std::endl << offset << std::endl;
// Software Guide : EndCodeSnippet
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();
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, Dimension>;
using CastFilterType =
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName(argv[4]);
caster->SetInput(resample->GetOutput());
writer->SetInput(caster->GetOutput());
writer->Update();
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 >= 6)
{
difference->SetInput1(fixedImageReader->GetOutput());
difference->SetInput2(resample->GetOutput());
writer2->SetFileName(argv[5]);
writer2->Update();
}
// Compute the difference image between the
// fixed and moving image before registration.
if (argc >= 7)
{
writer2->SetFileName(argv[6]);
difference->SetInput1(fixedImageReader->GetOutput());
difference->SetInput2(movingImageReader->GetOutput());
writer2->Update();
}
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
itkEuler2DTransform.h
itkRegularStepGradientDescentOptimizerv4.h
itkCenteredTransformInitializer.h
itkImageFileReader.h
itk::SmartPointer< Self >
itkCastImageFilter.h
itkImageRegistrationMethodv4.h
itkMeanSquaresImageToImageMetricv4.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::RegularStepGradientDescentOptimizerv4
Regular Step Gradient descent optimizer.
Definition: itkRegularStepGradientDescentOptimizerv4.h:47
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
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
itk::Euler2DTransform
Euler2DTransform of a vector space (e.g. space coordinates)
Definition: itkEuler2DTransform.h:41
itkImageFileWriter.h
itkSquaredDifferenceImageFilter.h
itk::ImageMaskSpatialObject
Implementation of an image mask as spatial object.
Definition: itkImageMaskSpatialObject.h:45
itkImageMaskSpatialObject.h
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
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
itk::CenteredTransformInitializer
CenteredTransformInitializer is a helper class intended to initialize the center of rotation and the ...
Definition: itkCenteredTransformInitializer.h:61