ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/ModelToImageRegistration1.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.
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*=========================================================================*/
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{SpatialObject} as a
// component of the registration framework in order to perform model based
// registration. The current example creates a geometrical model composed of
// several ellipses. Then, it uses the model to produce a synthetic binary
// image of the ellipses. Next, it introduces perturbations on the position
// and shape of the model, and finally it uses the perturbed version as the
// input to a registration problem. A metric is defined to evaluate the
// fitness between the geometric model and the image.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// Let's look first at the classes required to support
// SpatialObject. In this example we use the
// \doxygen{EllipseSpatialObject} as the basic shape components and we use
// the \doxygen{GroupSpatialObject} to group them together as a
// representation of a more complex shape. Their respective headers are
// included below.
//
// \index{itk::EllipseSpatialObject!header}
// \index{itk::GroupSpatialObject!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In order to generate the initial synthetic image of the ellipses, we use
// the \doxygen{SpatialObjectToImageFilter} that tests---for every pixel in
// the image---whether the pixel (and hence the spatial object) is
// \emph{inside} or \emph{outside} the geometric model.
//
// \index{itk::Spatial\-Object\-To\-Image\-Filter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// A metric is defined to evaluate the fitness between the
// SpatialObject and the Image. The base class for this
// type of metric is the \doxygen{ImageToSpatialObjectMetric}, whose header
// is included below.
//
// \index{itk::Image\-To\-Spatial\-Object\-Metric!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As in previous registration problems, we have to evaluate the image
// intensity in non-grid positions. The
// \doxygen{LinearInterpolateImageFunction} is used here for this purpose.
//
// \index{itk::Linear\-Interpolate\-Image\-Function!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The SpatialObject is mapped from its own space into the image
// space by using a \doxygen{Transform}. In this
// example, we use the \doxygen{Euler2DTransform}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Registration is fundamentally an optimization problem. Here we include
// the optimizer used to search the parameter space and identify the best
// transformation that will map the shape model on top of the image. The
// optimizer used in this example is the
// \doxygen{OnePlusOneEvolutionaryOptimizer} that implements an
// \href{http://www.aic.nrl.navy.mil/galist/}{evolutionary algorithm}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As in previous registration examples, it is important to
// track the evolution of the optimizer as it progresses through the
// parameter space. This is done by using the Command/Observer paradigm. The
// following lines of code implement the \doxygen{Command} observer that
// monitors the progress of the registration. The code is quite
// similar to what we have used in previous registration examples.
//
// \index{Model to Image Registration!Observer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkCommand.h"
template <class TOptimizer>
class IterationCallback : public itk::Command
{
public:
using Self = IterationCallback;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
using ConstPointer = itk::SmartPointer<const Self>;
itkTypeMacro(IterationCallback, Superclass);
itkNewMacro(Self);
using OptimizerType = TOptimizer;
void
SetOptimizer(OptimizerType * optimizer)
{
m_Optimizer = optimizer;
m_Optimizer->AddObserver(itk::IterationEvent(), this);
}
void
Execute(itk::Object * caller, const itk::EventObject & event) override
{
Execute((const itk::Object *)caller, event);
}
void
Execute(const itk::Object *, const itk::EventObject & event) override
{
if (typeid(event) == typeid(itk::StartEvent))
{
std::cout << std::endl << "Position Value";
std::cout << std::endl << std::endl;
}
else if (typeid(event) == typeid(itk::IterationEvent))
{
std::cout << m_Optimizer->GetCurrentIteration() << " ";
std::cout << m_Optimizer->GetValue() << " ";
std::cout << m_Optimizer->GetCurrentPosition() << std::endl;
}
else if (typeid(event) == typeid(itk::EndEvent))
{
std::cout << std::endl << std::endl;
std::cout << "After " << m_Optimizer->GetCurrentIteration();
std::cout << " iterations " << std::endl;
std::cout << "Solution is = " << m_Optimizer->GetCurrentPosition();
std::cout << std::endl;
}
}
// Software Guide : EndCodeSnippet
protected:
IterationCallback() = default;
};
// Software Guide : BeginLatex
//
// This command will be invoked at every iteration of the optimizer and will
// print out the current combination of transform parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// Consider now the most critical component of this new registration
// approach: the metric. This component evaluates the match between the
// SpatialObject and the Image. The
// smoothness and regularity of the metric determine the difficulty of the
// task assigned to the optimizer. In this case, we use a very robust
// optimizer that should be able to find its way even in the most
// discontinuous cost functions. The metric to be implemented should derive
// from the ImageToSpatialObjectMetric class.
//
// The following code implements a simple metric that computes the sum of
// the pixels that are inside the spatial object. In fact, the metric
// maximum is obtained when the model and the image are aligned. The metric
// is templated over the type of the SpatialObject and the type of
// the Image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
template <typename TFixedImage, typename TMovingSpatialObject>
class SimpleImageToSpatialObjectMetric
: public itk::ImageToSpatialObjectMetric<TFixedImage, TMovingSpatialObject>
{
// Software Guide : EndCodeSnippet
public:
using Self = SimpleImageToSpatialObjectMetric;
using Superclass =
using Pointer = itk::SmartPointer<Self>;
using ConstPointer = itk::SmartPointer<const Self>;
using PointListType = std::list<PointType>;
using MovingSpatialObjectType = TMovingSpatialObject;
using ParametersType = typename Superclass::ParametersType;
using DerivativeType = typename Superclass::DerivativeType;
using MeasureType = typename Superclass::MeasureType;
itkNewMacro(Self);
itkTypeMacro(SimpleImageToSpatialObjectMetric, ImageToSpatialObjectMetric);
static constexpr unsigned int ParametricSpaceDimension = 3;
void
SetMovingSpatialObject(const MovingSpatialObjectType * object) override
{
if (!this->m_FixedImage)
{
std::cout << "Please set the image before the moving spatial object"
<< std::endl;
return;
}
this->m_MovingSpatialObject = object;
m_PointList.clear();
using myIteratorType =
myIteratorType it(this->m_FixedImage,
this->m_FixedImage->GetBufferedRegion());
while (!it.IsAtEnd())
{
this->m_FixedImage->TransformIndexToPhysicalPoint(it.GetIndex(), point);
if (this->m_MovingSpatialObject->IsInsideInWorldSpace(point, 99999))
{
m_PointList.push_back(point);
}
++it;
}
std::cout << "Number of points in the metric = "
<< static_cast<unsigned long>(m_PointList.size()) << std::endl;
}
void
GetDerivative(const ParametersType &, DerivativeType &) const override
{
return;
}
// Software Guide : BeginLatex
//
// The fundamental operation of the metric is its \code{GetValue()} method.
// It is in this method that the fitness value is computed. In our current
// example, the fitness is computed over the points of the
// SpatialObject. For each point, its coordinates are mapped
// through the transform into image space. The resulting point is used
// to evaluate the image and the resulting value is accumulated in a sum.
// Since we are not allowing scale changes, the optimal value of the sum
// will result when all the SpatialObject points are mapped on
// the white regions of the image. Note that the argument for the
// \code{GetValue()} method is the array of parameters of the transform.
//
// \index{Image\-To\-Spatial\-Object\-Metric!GetValue()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
MeasureType
GetValue(const ParametersType & parameters) const override
{
double value;
this->m_Transform->SetParameters(parameters);
value = 0;
for (auto it : m_PointList)
{
PointType transformedPoint = this->m_Transform->TransformPoint(it);
if (this->m_Interpolator->IsInsideBuffer(transformedPoint))
{
value += this->m_Interpolator->Evaluate(transformedPoint);
}
}
return value;
}
// Software Guide : EndCodeSnippet
void
GetValueAndDerivative(const ParametersType & parameters,
MeasureType & Value,
DerivativeType & Derivative) const override
{
Value = this->GetValue(parameters);
this->GetDerivative(parameters, Derivative);
}
private:
PointListType m_PointList;
};
// Software Guide : BeginLatex
//
// Having defined all the registration components we are ready to put the
// pieces together and implement the registration process.
//
// Software Guide : EndLatex
int
main(int argc, char * argv[])
{
if (argc > 1)
{
std::cerr << "Too many parameters " << std::endl;
std::cerr << "Usage: " << argv[0] << std::endl;
}
// Software Guide : BeginLatex
//
// First we instantiate the GroupSpatialObject and
// EllipseSpatialObject. These two objects are parameterized by
// the dimension of the space. In our current example a $2D$ instantiation
// is created.
//
// \index{Group\-Spatial\-Object!Instantiation}
// \index{Ellipse\-Spatial\-Object!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using GroupType = itk::GroupSpatialObject<2>;
using EllipseType = itk::EllipseSpatialObject<2>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The image is instantiated in the following lines using the pixel
// type and the space dimension. This image uses a \code{float} pixel
// type since we plan to blur it in order to increase the capture radius of
// the optimizer. Images of real pixel type behave better under blurring
// than those of integer pixel type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using ImageType = itk::Image<float, 2>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Here is where the fun begins! In the following lines we create the
// EllipseSpatialObjects using their \code{New()} methods, and
// assigning the results to SmartPointers. These lines will create
// three ellipses.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
EllipseType::Pointer ellipse1 = EllipseType::New();
EllipseType::Pointer ellipse2 = EllipseType::New();
EllipseType::Pointer ellipse3 = EllipseType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Every class deriving from SpatialObject has particular
// parameters enabling the user to tailor its shape. In the case of the
// EllipseSpatialObject, \code{SetRadius()} is used to
// define the ellipse size. An additional \code{SetRadius(Array)} method
// allows the user to define the ellipse axes independently.
//
// \index{itk::EllipseSpatialObject!SetRadius()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ellipse1->SetRadiusInObjectSpace(10.0);
ellipse2->SetRadiusInObjectSpace(10.0);
ellipse3->SetRadiusInObjectSpace(10.0);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The ellipses are created centered in space by default. We use the
// following lines of code to arrange the ellipses in a triangle.
// The spatial transform intrinsically associated with the object is
// accessed by the \code{GetTransform()} method. This transform can define
// a translation in space with the \code{SetOffset()} method. We take
// advantage of this feature to place the ellipses at particular
// points in space.
//
// Software Guide : EndLatex
// Place each ellipse at the right position to form a triangle
// Software Guide : BeginCodeSnippet
EllipseType::TransformType::OffsetType offset;
offset[0] = 100.0;
offset[1] = 40.0;
ellipse1->GetModifiableObjectToParentTransform()->SetOffset(offset);
ellipse1->Update();
offset[0] = 40.0;
offset[1] = 150.0;
ellipse2->GetModifiableObjectToParentTransform()->SetOffset(offset);
ellipse2->Update();
offset[0] = 150.0;
offset[1] = 150.0;
ellipse3->GetModifiableObjectToParentTransform()->SetOffset(offset);
ellipse3->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Note that after a change has been made in the transform, the
// SpatialObject invokes the method
// \code{ComputeGlobalTransform()} in order to update its global
// transform. The reason for doing this is that SpatialObjects
// can be arranged in hierarchies. It is then possible to change the
// position of a set of spatial objects by moving the parent of the group.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// Now we add the three EllipseSpatialObjects to a
// GroupSpatialObject that will be subsequently passed on to the
// registration method. The GroupSpatialObject facilitates the
// management of the three ellipses as a higher level structure
// representing a complex shape. Groups can be nested any number of levels
// in order to represent shapes with higher detail.
//
// \index{itk::GroupSpatialObject!New()}
// \index{itk::GroupSpatialObject!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
GroupType::Pointer group = GroupType::New();
group->AddChild(ellipse1);
group->AddChild(ellipse2);
group->AddChild(ellipse3);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Having the geometric model ready, we proceed to generate the binary
// image representing the imprint of the space occupied by the ellipses.
// The SpatialObjectToImageFilter is used to that end. Note that
// this filter is instantiated over the spatial object used and the image
// type to be generated.
//
//
// \index{itk::Spatial\-Object\-To\-Image\-Filter!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using SpatialObjectToImageFilterType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// With the defined type, we construct a filter using the \code{New()}
// method. The newly created filter is assigned to a SmartPointer.
//
// \index{itk::SpatialObjectToImageFilter!New()}
// \index{itk::SpatialObjectToImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
SpatialObjectToImageFilterType::Pointer imageFilter =
SpatialObjectToImageFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The GroupSpatialObject is passed as input to the filter.
//
// \index{itk::SpatialObjectToImageFilter!SetInput()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
imageFilter->SetInput(group);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The \doxygen{SpatialObjectToImageFilter} acts as a resampling filter.
// Therefore it requires the user to define the size of the desired output
// image. This is specified with the \code{SetSize()} method.
//
// \index{itk::SpatialObjectToImageFilter!SetSize()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
size[0] = 200;
size[1] = 200;
imageFilter->SetSize(size);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally we trigger the execution of the filter by calling the
// \code{Update()} method.
//
// \index{itk::SpatialObjectToImageFilter!Update()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
imageFilter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In order to obtain a smoother metric, we blur the image using a
// \doxygen{DiscreteGaussianImageFilter}. This extends the capture radius
// of the metric and produce a more continuous cost function to
// optimize. The following lines instantiate the Gaussian filter and
// create one object of this type using the \code{New()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using GaussianFilterType =
GaussianFilterType::Pointer gaussianFilter = GaussianFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output of the SpatialObjectToImageFilter is connected as
// input to the DiscreteGaussianImageFilter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
gaussianFilter->SetInput(imageFilter->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The variance of the filter is defined as a large value in order to
// increase the capture radius. Finally the execution of the filter is
// triggered using the \code{Update()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr double variance = 20;
gaussianFilter->SetVariance(variance);
gaussianFilter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Below we instantiate the type of the
// \doxygen{ImageToSpatialObjectRegistrationMethod} method and instantiate
// a registration object with the \code{New()} method. Note that the
// registration type is templated over the Image and the
// SpatialObject types. The spatial object in this case is the
// group of spatial objects.
//
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!Instantiation}
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!New()}
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using RegistrationType =
RegistrationType::Pointer registration = RegistrationType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now we instantiate the metric that is templated over
// the image type and the spatial object type. As usual, the \code{New()}
// method is used to create an object.
//
// \index{itk::Image\-To\-Spatial\-Object\-Metric!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using MetricType = SimpleImageToSpatialObjectMetric<ImageType, GroupType>;
MetricType::Pointer metric = MetricType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// An interpolator will be needed to evaluate the image at non-grid
// positions. Here we instantiate a linear interpolator type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InterpolatorType =
InterpolatorType::Pointer interpolator = InterpolatorType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The following lines instantiate the evolutionary optimizer.
//
// \index{itk::One\-Plus\-One\-Evolutionary\-Optimizer!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OptimizerType = itk::OnePlusOneEvolutionaryOptimizer;
OptimizerType::Pointer optimizer = OptimizerType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next, we instantiate the transform class. In this case we use the
// Euler2DTransform that implements a rigid transform in $2D$
// space.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using TransformType = itk::Euler2DTransform<>;
TransformType::Pointer transform = TransformType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Evolutionary algorithms are based on testing random variations
// of parameters. In order to support the computation of random values,
// ITK provides a family of random number generators. In this example, we
// use the \doxygen{NormalVariateGenerator} which generates values with a
// normal distribution.
//
// \index{itk::NormalVariateGenerator!New()}
// \index{itk::NormalVariateGenerator!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The random number generator must be initialized with a seed.
//
// \index{itk::NormalVariateGenerator!Initialize()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
generator->Initialize(12345);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The OnePlusOneEvolutionaryOptimizer is initialized by
// specifying the random number generator, the number of samples for the
// initial population and the maximum number of iterations.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetNormalVariateGenerator(generator);
optimizer->Initialize(10);
optimizer->SetMaximumIteration(400);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As in previous registration examples, we take care to normalize the
// dynamic range of the different transform parameters. In particular, the
// we must compensate for the ranges of the angle and translations of the
// Euler2DTransform. In order to achieve this goal, we provide an array of
// scales to the optimizer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::ParametersType parametersScale;
parametersScale.set_size(3);
parametersScale[0] = 1000; // angle scale
for (unsigned int i = 1; i < 3; i++)
{
parametersScale[i] = 2; // offset scale
}
optimizer->SetScales(parametersScale);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Here we instantiate the Command object that will act as an
// observer of the registration method and print out parameters at each
// iteration. Earlier, we defined this command as a class templated over
// the optimizer type. Once it is created with the \code{New()} method, we
// connect the optimizer to the command.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using IterationCallbackType = IterationCallback<OptimizerType>;
IterationCallbackType::Pointer callback = IterationCallbackType::New();
callback->SetOptimizer(optimizer);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// All the components are plugged into the
// ImageToSpatialObjectRegistrationMethod object. The typical
// \code{Set()} methods are used here. Note the use of the
// \code{SetMovingSpatialObject()} method for connecting the spatial
// object. We provide the blurred version of the original synthetic binary
// image as the input image.
//
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!SetFixedImage()}
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!SetMovingSpatialObject()}
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!SetTransform()}
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!SetInterpolator()}
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!SetOptimizer()}
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!SetMetric()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetFixedImage(gaussianFilter->GetOutput());
registration->SetMovingSpatialObject(group);
registration->SetTransform(transform);
registration->SetInterpolator(interpolator);
registration->SetOptimizer(optimizer);
registration->SetMetric(metric);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The initial set of transform parameters is passed to the registration
// method using the \code{SetInitialTransformParameters()} method. Note
// that since our original model is already registered with the synthetic
// image, we introduce an artificial mis-registration in order to
// initialize the optimization at some point away from the optimal value.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::ParametersType initialParameters(
transform->GetNumberOfParameters());
initialParameters[0] = 0.2; // Angle
initialParameters[1] = 7.0; // Offset X
initialParameters[2] = 6.0; // Offset Y
registration->SetInitialTransformParameters(initialParameters);
// Software Guide : EndCodeSnippet
std::cout << "Initial Parameters : " << initialParameters << std::endl;
// Software Guide : BeginLatex
//
// Due to the character of the metric used to evaluate the fitness
// between the spatial object and the image, we must tell the optimizer
// that we are interested in finding the maximum value of the metric. Some
// metrics associate low numeric values with good matching, while others
// associate high numeric values with good matching. The
// \code{MaximizeOn()} and \code{MaximizeOff()} methods allow the user to
// deal with both types of metrics.
//
// \index{itk::Optimizer!MaximizeOn()}
// \index{itk::Optimizer!MaximizeOff()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->MaximizeOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, we trigger the execution of the registration process with the
// \code{Update()} method. We place this call in a
// \code{try/catch} block in case any exception is thrown during the
// process.
//
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!Update()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
registration->Update();
std::cout << "Optimizer stop condition: "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch (const itk::ExceptionObject & exp)
{
std::cerr << "Exception caught ! " << std::endl;
std::cerr << exp << std::endl;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The set of transform parameters resulting from the registration can be
// recovered with the \code{GetLastTransformParameters()} method. This
// method returns the array of transform parameters that should be
// interpreted according to the implementation of each transform. In our
// current example, the Euler2DTransform has three parameters:
// the rotation angle, the translation in $x$ and the translation in $y$.
//
// \index{itk::Image\-To\-Spatial\-Object\-Registration\-Method!Update()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
RegistrationType::ParametersType finalParameters =
registration->GetLastTransformParameters();
std::cout << "Final Solution is : " << finalParameters << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[height=0.44\textwidth]{ModelToImageRegistrationTraceAngle}
// \includegraphics[height=0.44\textwidth]{ModelToImageRegistrationTraceTranslations}
// \itkcaption[SpatialObject to Image Registration results]{Plots of the
// angle and translation parameters for a registration process between an
// spatial object and an image.}
// \label{fig:ModelToImageRegistrationPlots}
// \end{figure}
//
// The results are presented in
// Figure~\ref{fig:ModelToImageRegistrationPlots}. The left side shows the
// evolution of the angle parameter as a function of iteration
// numbers, while the right side shows the $(x,y)$ translation.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itk::DiscreteGaussianImageFilter
Blurs an image by separable convolution with discrete gaussian kernels. This filter performs Gaussian...
Definition: itkDiscreteGaussianImageFilter.h:63
itk::ImageToSpatialObjectRegistrationMethod
Base class for Image Registration Methods.
Definition: itkImageToSpatialObjectRegistrationMethod.h:85
itk::SingleValuedCostFunction::MeasureType
double MeasureType
Definition: itkSingleValuedCostFunction.h:50
itkImageToSpatialObjectMetric.h
itkEuler2DTransform.h
itk::ImageRegionConstIteratorWithIndex
A multi-dimensional iterator templated over image type that walks an image region and is specialized ...
Definition: itkImageRegionConstIteratorWithIndex.h:128
itkLinearInterpolateImageFunction.h
itk::ImageToSpatialObjectMetric::GetValueAndDerivative
void GetValueAndDerivative(const ParametersType &parameters, MeasureType &Value, DerivativeType &Derivative) const override=0
itkEllipseSpatialObject.h
itk::GTest::TypedefsAndConstructors::Dimension2::PointType
ImageBaseType::PointType PointType
Definition: itkGTestTypedefsAndConstructors.h:51
itkImageFileReader.h
itk::GTest::TypedefsAndConstructors::Dimension2::SizeType
ImageBaseType::SizeType SizeType
Definition: itkGTestTypedefsAndConstructors.h:49
itk::SpatialObjectToImageFilter
Base class for filters that take a SpatialObject as input and produce an image as output....
Definition: itkSpatialObjectToImageFilter.h:41
itk::SmartPointer< Self >
itkImageToSpatialObjectRegistrationMethod.h
itkCastImageFilter.h
itk::SingleValuedCostFunction::GetDerivative
virtual void GetDerivative(const ParametersType &parameters, DerivativeType &derivative) const =0
itk::EllipseSpatialObject
Definition: itkEllipseSpatialObject.h:38
itk::ImageToSpatialObjectMetric::SetMovingSpatialObject
virtual void SetMovingSpatialObject(const MovingSpatialObjectType *_arg)
itk::LinearInterpolateImageFunction
Linearly interpolate an image at specified positions.
Definition: itkLinearInterpolateImageFunction.h:50
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:44
itkGroupSpatialObject.h
itkSpatialObjectToImageFilter.h
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:41
itkOnePlusOneEvolutionaryOptimizer.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkRescaleIntensityImageFilter.h
itk::Euler2DTransform
Euler2DTransform of a vector space (e.g. space coordinates)
Definition: itkEuler2DTransform.h:41
itkImageFileWriter.h
itk::SingleValuedCostFunction::ParametersType
Superclass::ParametersType ParametersType
Definition: itkSingleValuedCostFunction.h:54
itk::Size::SetSize
void SetSize(const SizeValueType val[VDimension])
Definition: itkSize.h:179
itk::WeakPointer< OptimizerType >
itkNormalVariateGenerator.h
itk::Object
Base class for most ITK classes.
Definition: itkObject.h:60
itk::SingleValuedCostFunction::GetValue
virtual MeasureType GetValue(const ParametersType &parameters) const =0
itk::Point< double, 2 >
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::OnePlusOneEvolutionaryOptimizer
1+1 evolutionary strategy optimizer
Definition: itkOnePlusOneEvolutionaryOptimizer.h:71
itk::SingleValuedCostFunction::DerivativeType
Array< ParametersValueType > DerivativeType
Definition: itkSingleValuedCostFunction.h:59
itk::GroupSpatialObject
Representation of a group based on the spatial object classes.
Definition: itkGroupSpatialObject.h:39
itkCommand.h
itkDiscreteGaussianImageFilter.h
itk::ImageToSpatialObjectMetric
Computes similarity between a moving spatial object and an Image to be registered.
Definition: itkImageToSpatialObjectMetric.h:61
itk::Statistics::NormalVariateGenerator::New
static Pointer New()