ITK  5.1.0
Insight Toolkit
Examples/RegistrationITKv4/DeformableRegistration2.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: RatLungSlice1.mha
// INPUTS: RatLungSlice2.mha
// ARGUMENTS: DeformableRegistration2Output.mha
// ARGUMENTS: DeformableRegistration2Field.mha
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example demonstrates how to use the ``demons'' algorithm to deformably
// register two images. The first step is to include the header files.
//
// 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.
//
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
itkNewMacro(CommandIterationUpdate);
protected:
CommandIterationUpdate() = default;
using InternalImageType = itk::Image<float, 2>;
using VectorPixelType = itk::Vector<float, 2>;
using DisplacementFieldType = itk::Image<VectorPixelType, 2>;
using RegistrationFilterType = itk::DemonsRegistrationFilter<InternalImageType,
InternalImageType,
DisplacementFieldType>;
public:
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
{
const auto * filter = static_cast<const RegistrationFilterType *>(object);
if (!(itk::IterationEvent().CheckEvent(&event)))
{
return;
}
std::cout << filter->GetMetric() << 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 ";
std::cerr << " outputImageFile " << std::endl;
std::cerr << " [outputDisplacementFieldFile] " << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Second, we declare the types of the images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr unsigned int Dimension = 2;
using PixelType = unsigned short;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
// Software Guide : EndCodeSnippet
// Set up the file readers
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]);
// Software Guide : BeginLatex
//
// Image file readers are set up in a similar fashion to previous examples.
// To support the re-mapping of the moving image intensity, we declare an
// internal image type with a floating point pixel type and cast the input
// images to the internal image type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InternalPixelType = float;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
using MovingImageCasterType =
FixedImageCasterType::Pointer fixedImageCaster = FixedImageCasterType::New();
MovingImageCasterType::Pointer movingImageCaster = MovingImageCasterType::New();
fixedImageCaster->SetInput(fixedImageReader->GetOutput());
movingImageCaster->SetInput(movingImageReader->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The demons algorithm relies on the assumption that pixels representing the
// same homologous point on an object have the same intensity on both the
// fixed and moving images to be registered. In this example, we will
// preprocess the moving image to match the intensity between the images
// using the \doxygen{HistogramMatchingImageFilter}.
//
// \index{itk::HistogramMatchingImageFilter}
//
// The basic idea is to match the histograms of the two images at a
// user-specified number of quantile values. For robustness, the histograms
// are matched so that the background pixels are excluded from both
// histograms. For MR images, a simple procedure is to exclude all gray
// values that are smaller than the mean gray value of the image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using MatchingFilterType =
MatchingFilterType::Pointer matcher = MatchingFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// For this example, we set the moving image as the source or input image and
// the fixed image as the reference image.
//
// \index{itk::HistogramMatchingImageFilter!SetInput()}
// \index{itk::HistogramMatchingImageFilter!SetSourceImage()}
// \index{itk::HistogramMatchingImageFilter!SetReferenceImage()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
matcher->SetInput(movingImageCaster->GetOutput());
matcher->SetReferenceImage(fixedImageCaster->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We then select the number of bins to represent the histograms and the
// number of points or quantile values where the histogram is to be
// matched.
//
// \index{itk::HistogramMatchingImageFilter!SetNumberOfHistogramLevels()}
// \index{itk::HistogramMatchingImageFilter!SetNumberOfMatchPoints()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
matcher->SetNumberOfHistogramLevels(1024);
matcher->SetNumberOfMatchPoints(7);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Simple background extraction is done by thresholding at the mean
// intensity.
//
// \index{itk::HistogramMatchingImageFilter!ThresholdAtMeanIntensityOn()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
matcher->ThresholdAtMeanIntensityOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In the \doxygen{DemonsRegistrationFilter}, the deformation field is
// represented as an image whose pixels are floating point vectors.
//
// \index{itk::DemonsRegistrationFilter}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using VectorPixelType = itk::Vector<float, Dimension>;
using DisplacementFieldType = itk::Image<VectorPixelType, Dimension>;
using RegistrationFilterType = itk::DemonsRegistrationFilter<InternalImageType,
InternalImageType,
DisplacementFieldType>;
RegistrationFilterType::Pointer filter = RegistrationFilterType::New();
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the registration filter.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
filter->AddObserver(itk::IterationEvent(), observer);
// Software Guide : BeginLatex
//
// The input fixed image is simply the output of the fixed image casting
// filter. The input moving image is the output of the histogram matching
// filter.
//
// \index{itk::DemonsRegistrationFilter!SetFixedImage()}
// \index{itk::DemonsRegistrationFilter!SetMovingImage()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetFixedImage(fixedImageCaster->GetOutput());
filter->SetMovingImage(matcher->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The demons registration filter has two parameters: the number of
// iterations to be performed and the standard deviation of the Gaussian
// smoothing kernel to be applied to the deformation field after each
// iteration.
// \index{itk::DemonsRegistrationFilter!SetNumberOfIterations()}
// \index{itk::DemonsRegistrationFilter!SetStandardDeviations()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetNumberOfIterations(50);
filter->SetStandardDeviations(1.0);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The registration algorithm is triggered by updating the filter. The
// filter output is the computed deformation field.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The \doxygen{ResampleImageFilter} can be used to warp the moving image with
// the output deformation field. The default interpolator of the
// \doxygen{ResampleImageFilter}, is used but specification of the output image
// spacing and origin are required.
//
// \index{itk::ResampleImageFilter}
// \index{itk::ResampleImageFilter!SetInput()}
// \index{itk::ResampleImageFilter!SetInterpolator()}
// \index{itk::ResampleImageFilter!SetOutputSpacing()}
// \index{itk::ResampleImageFilter!SetOutputOrigin()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using InterpolatorPrecisionType = double;
using WarperType = itk::ResampleImageFilter<MovingImageType,
OutputImageType,
InterpolatorPrecisionType,
float>;
WarperType::Pointer warper = WarperType::New();
warper->SetInput(movingImageReader->GetOutput());
warper->UseReferenceImageOn();
warper->SetReferenceImage(fixedImageReader->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The \code{ResampleImageFilter} requires a transform, so a
// \doxygen{DisplacementFieldTransform} must be constructed then set
// as the transform of the \code{ResampleImageFilter}. The resulting
// warped or resampled image is written to file as per previous
// examples.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using DisplacementFieldTransformType =
auto displacementTransform = DisplacementFieldTransformType::New();
displacementTransform->SetDisplacementField(filter->GetOutput());
warper->SetTransform(displacementTransform);
// Software Guide : EndCodeSnippet
// Write warped image out to file
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv[3]);
writer->SetInput(warper->GetOutput());
writer->Update();
// Software Guide : BeginLatex
//
// Let's execute this example using the rat lung data from the previous example.
// The associated data files can be found in \code{Examples/Data}:
//
// \begin{itemize}
// \item \code{RatLungSlice1.mha}
// \item \code{RatLungSlice2.mha}
// \end{itemize}
//
// \begin{figure} \center
// \includegraphics[width=0.44\textwidth]{DeformableRegistration2CheckerboardBefore}
// \includegraphics[width=0.44\textwidth]{DeformableRegistration2CheckerboardAfter}
// \itkcaption[Demon's deformable registration output]{Checkerboard comparisons
// before and after demons-based deformable registration.}
// \label{fig:DeformableRegistration2Output}
// \end{figure}
//
// The result of the demons-based deformable registration is presented in
// Figure \ref{fig:DeformableRegistration2Output}. The checkerboard
// comparison shows that the algorithm was able to recover the misalignment
// due to expiration.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// It may be also desirable to write the deformation field as an image of
// vectors. This can be done with the following code.
//
// Software Guide : EndLatex
if (argc > 4) // if a fourth line argument has been provided...
{
// Software Guide : BeginCodeSnippet
FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
fieldWriter->SetFileName(argv[4]);
fieldWriter->SetInput(filter->GetOutput());
fieldWriter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Note that the file format used for writing the deformation field must be
// capable of representing multiple components per pixel. This is the case
// for the MetaImage and VTK file formats for example.
//
// Software Guide : EndLatex
}
if (argc > 5) // if a fifth line argument has been provided...
{
using VectorImage2DType = DisplacementFieldType;
using Vector2DType = DisplacementFieldType::PixelType;
VectorImage2DType::ConstPointer vectorImage2D = filter->GetOutput();
VectorImage2DType::RegionType region2D = vectorImage2D->GetBufferedRegion();
VectorImage2DType::IndexType index2D = region2D.GetIndex();
VectorImage2DType::SizeType size2D = region2D.GetSize();
using Vector3DType = itk::Vector<float, 3>;
using VectorImage3DType = itk::Image<Vector3DType, 3>;
using VectorImage3DWriterType = itk::ImageFileWriter<VectorImage3DType>;
VectorImage3DWriterType::Pointer writer3D = VectorImage3DWriterType::New();
VectorImage3DType::Pointer vectorImage3D = VectorImage3DType::New();
index3D[0] = index2D[0];
index3D[1] = index2D[1];
index3D[2] = 0;
size3D[0] = size2D[0];
size3D[1] = size2D[1];
size3D[2] = 1;
region3D.SetSize(size3D);
region3D.SetIndex(index3D);
vectorImage3D->SetRegions(region3D);
vectorImage3D->Allocate();
Iterator2DType it2(vectorImage2D, region2D);
Iterator3DType it3(vectorImage3D, region3D);
it2.GoToBegin();
it3.GoToBegin();
Vector2DType vector2D;
Vector3DType vector3D;
vector3D[2] = 0; // set Z component to zero.
while (!it2.IsAtEnd())
{
vector2D = it2.Get();
vector3D[0] = vector2D[0];
vector3D[1] = vector2D[1];
it3.Set(vector3D);
++it2;
++it3;
}
writer3D->SetInput(vectorImage3D);
writer3D->SetFileName(argv[5]);
try
{
writer3D->Update();
}
catch (itk::ExceptionObject & excp)
{
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
}
return EXIT_SUCCESS;
}
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itkDisplacementFieldTransform.h
itk::HistogramMatchingImageFilter
Normalize the grayscale values for a source image by matching the shape of the source image histogram...
Definition: itkHistogramMatchingImageFilter.h:75
itkHistogramMatchingImageFilter.h
itkDemonsRegistrationFilter.h
itk::Vector
A templated class holding a n-Dimensional vector.
Definition: itkVector.h:62
itkImageFileReader.h
itk::GTest::TypedefsAndConstructors::Dimension2::SizeType
ImageBaseType::SizeType SizeType
Definition: itkGTestTypedefsAndConstructors.h:49
itk::SmartPointer
Implements transparent reference counting.
Definition: itkSmartPointer.h:51
itkImageRegionIterator.h
itkCastImageFilter.h
itk::DisplacementFieldTransform
Provides local/dense/high-dimensionality transformation via a a displacement field.
Definition: itkDisplacementFieldTransform.h:86
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::ImageRegionIterator
A multi-dimensional iterator templated over image type that walks a region of pixels.
Definition: itkImageRegionIterator.h:78
itk::GTest::TypedefsAndConstructors::Dimension2::IndexType
ImageBaseType::IndexType IndexType
Definition: itkGTestTypedefsAndConstructors.h:50
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:44
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::Index::GetIndex
const IndexValueType * GetIndex() const
Definition: itkIndex.h:228
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:41
itk::GTest::TypedefsAndConstructors::Dimension2::RegionType
ImageBaseType::RegionType RegionType
Definition: itkGTestTypedefsAndConstructors.h:54
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkImageFileWriter.h
itk::Size::SetSize
void SetSize(const SizeValueType val[VDimension])
Definition: itkSize.h:179
itk::DemonsRegistrationFilter
Deformably register two images using the demons algorithm.
Definition: itkDemonsRegistrationFilter.h:63
itk::ResampleImageFilter
Resample an image via a coordinate transform.
Definition: itkResampleImageFilter.h:89
itk::Object
Base class for most ITK classes.
Definition: itkObject.h:60
itk::ImageRegionConstIterator
A multi-dimensional iterator templated over image type that walks a region of pixels.
Definition: itkImageRegionConstIterator.h:107
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
itk::ImageSource::GetOutput
OutputImageType * GetOutput()
itkResampleImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itk::Size::GetSize
const SizeValueType * GetSize() const
Definition: itkSize.h:169