ITK  5.2.0 Insight Toolkit
Examples/Segmentation/ShapeDetectionLevelSetFilter.cxx
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput1.png}
// ARGUMENTS: 81 114 5 1.0 -0.5 3.0 .05 1
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput1Smoothing.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput1Sigmoid.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput1FastMarching.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput2.png}
// ARGUMENTS: 99 114 5 1.0 -0.5 3.0 .05 1
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput2Smoothing.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput2Sigmoid.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput2FastMarching.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput3.png}
// ARGUMENTS: 56 92 5 1.0 -0.3 2.0 .05 1
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput3Smoothing.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput3Sigmoid.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput3FastMarching.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput4.png}
// ARGUMENTS: 40 90 5 0.5 -0.3 2.0 .05 1
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput4Smoothing.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput4Sigmoid.png}
// OUTPUTS: {ShapeDetectionLevelSetFilterOutput4FastMarching.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The use of the \doxygen{ShapeDetectionLevelSetImageFilter} is illustrated
// in the following example. The implementation of this filter in ITK is
// based on the paper by Malladi et al \cite{Malladi1995}. In this
// implementation, the governing differential equation has an additional
// curvature-based term. This term acts as a smoothing term where areas of
// high curvature, assumed to be due to noise, are smoothed out. Scaling
// parameters are used to control the tradeoff between the expansion term and
// the smoothing term. One consequence of this additional curvature term is
// that the fast marching algorithm is no longer applicable, because the
// contour is no longer guaranteed to always be expanding. Instead, the level
// set function is updated iteratively.
//
// The ShapeDetectionLevelSetImageFilter expects two inputs,
// the first being an initial Level Set in the form of an
// \doxygen{Image}, and the second being a feature image. For this algorithm,
// the feature image is an edge potential image that basically
// follows the same rules applicable to the speed image used for the
// FastMarchingImageFilter discussed in
// Section~\ref{sec:FastMarchingImageFilter}.
//
// In this example we use an FastMarchingImageFilter to produce the initial
// level set as the distance function to a set of user-provided seeds. The
// FastMarchingImageFilter is run with a constant speed value which enables
// us to employ this filter as a distance map calculator.
//
// \begin{figure} \center
// \includegraphics[width=\textwidth]{ShapeDetectionCollaborationDiagram1}
// \itkcaption[ShapeDetectionLevelSetImageFilter collaboration
// diagram]{Collaboration diagram for the ShapeDetectionLevelSetImageFilter
// applied to a segmentation task.}
// \label{fig:ShapeDetectionCollaborationDiagram}
// \end{figure}
//
// Figure~\ref{fig:ShapeDetectionCollaborationDiagram} shows the major
// components involved in the application of the
// ShapeDetectionLevelSetImageFilter to a segmentation task. The first stage
// involves smoothing using the
// \doxygen{CurvatureAnisotropicDiffusionImageFilter}. The smoothed image is
// passed as the input for the
// \doxygen{GradientMagnitudeRecursiveGaussianImageFilter} and then to the
// \doxygen{SigmoidImageFilter} in order to produce the edge potential image.
// A set of user-provided seeds is passed to an FastMarchingImageFilter in
// order to compute the distance map. A constant value is subtracted from
// this map in order to obtain a level set in which the \emph{zero set}
// represents the initial contour. This level set is also passed as input to
// the ShapeDetectionLevelSetImageFilter.
//
// Finally, the level set at the output of the
// ShapeDetectionLevelSetImageFilter is passed to an
// BinaryThresholdImageFilter in order to produce a binary mask representing
// the segmented object.
//
// Let's start by including the headers of the main filters involved in the
// preprocessing.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The edge potential map is generated using these filters as in the previous
// example.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// We will need the Image class, the FastMarchingImageFilter class and the
// ShapeDetectionLevelSetImageFilter class. Hence we include their headers
// here.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The level set resulting from the ShapeDetectionLevelSetImageFilter will
// be thresholded at the zero level in order to get a binary image
// representing the segmented object. The BinaryThresholdImageFilter is used
// for this purpose.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// and \doxygen{ImageFileWriter}.
#include "itksys/SystemTools.hxx"
// The RescaleIntensityImageFilter is used to renormailize the output
// of filters before sending them to files.
int
main(int argc, char * argv[])
{
if (argc < 11)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage outputImage";
std::cerr << " seedX seedY InitialDistance";
std::cerr << " Sigma SigmoidAlpha SigmoidBeta ";
std::cerr << " curvatureScaling propagationScaling" << std::endl;
return EXIT_FAILURE;
}
const std::string inputImageFile(argv[1]);
const std::string outputImageFile(argv[2]);
// Software Guide : BeginLatex
//
// We now define the image type using a particular pixel type and a
// dimension. In this case the \code{float} type is used for the pixels
// due to the requirements of the smoothing filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InternalPixelType = float;
constexpr unsigned int Dimension = 2;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output image, on the other hand, is declared to be binary.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The type of the BinaryThresholdImageFilter filter is instantiated below
// using the internal image type and the output image type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using ThresholdingFilterType =
ThresholdingFilterType::Pointer thresholder = ThresholdingFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The upper threshold of the BinaryThresholdImageFilter is set
// to $0.0$ in order to display the zero set of the resulting level
// set. The lower threshold is set to a large negative number in order to
// ensure that the interior of the segmented object will appear
// inside the binary region.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
thresholder->SetLowerThreshold(-1000.0);
thresholder->SetUpperThreshold(0.0);
thresholder->SetOutsideValue(0);
thresholder->SetInsideValue(255);
// Software Guide : EndCodeSnippet
// We instantiate reader and writer types in the following lines.
//
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(outputImageFile);
// The RescaleIntensityImageFilter type is declared below. This filter will
// renormalize image before sending them to writers.
//
using CastFilterType =
// Software Guide : BeginLatex
//
// The CurvatureAnisotropicDiffusionImageFilter type is instantiated using
// the internal image type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using SmoothingFilterType =
InternalImageType>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filter is instantiated by invoking the \code{New()} method and
// assigning the result to a \doxygen{SmartPointer}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
SmoothingFilterType::Pointer smoothing = SmoothingFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The types of the GradientMagnitudeRecursiveGaussianImageFilter and
// SigmoidImageFilter are instantiated using the internal
// image type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InternalImageType>;
using SigmoidFilterType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The corresponding filter objects are created with the method
// \code{New()}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
SigmoidFilterType::Pointer sigmoid = SigmoidFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The minimum and maximum values of the SigmoidImageFilter output are
// defined with the methods \code{SetOutputMinimum()} and
// \code{SetOutputMaximum()}. In our case, we want these two values to be
// $0.0$ and $1.0$ respectively in order to get a nice speed image to feed
// to the FastMarchingImageFilter. Additional details on the use of the
// SigmoidImageFilter are presented in
// Section~\ref{sec:IntensityNonLinearMapping}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
sigmoid->SetOutputMinimum(0.0);
sigmoid->SetOutputMaximum(1.0);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We now declare the type of the FastMarchingImageFilter that
// will be used to generate the initial level set in the form of a distance
// map.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FastMarchingFilterType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next we construct one filter of this class using the \code{New()}
// method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FastMarchingFilterType::Pointer fastMarching =
FastMarchingFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In the following lines we instantiate the type of the
// ShapeDetectionLevelSetImageFilter and create an object of this type
// using the \code{New()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using ShapeDetectionFilterType =
InternalImageType>;
ShapeDetectionFilterType::Pointer shapeDetection =
ShapeDetectionFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filters are now connected in a pipeline indicated in
// Figure~\ref{fig:ShapeDetectionCollaborationDiagram} with the following
// code.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
shapeDetection->SetInput(fastMarching->GetOutput());
shapeDetection->SetFeatureImage(sigmoid->GetOutput());
thresholder->SetInput(shapeDetection->GetOutput());
writer->SetInput(thresholder->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The CurvatureAnisotropicDiffusionImageFilter requires a couple
// of parameters to be defined. The following are typical values for $2D$
// images. However they may have to be adjusted depending on the amount of
// noise present in the input image. This filter has been discussed in
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
smoothing->SetTimeStep(0.125);
smoothing->SetNumberOfIterations(5);
smoothing->SetConductanceParameter(9.0);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// equivalent of a convolution with a Gaussian kernel followed by a
// derivative operator. The sigma of this Gaussian can be used to control
// the range of influence of the image edges. This filter has been
// discussed in
//
//
// Software Guide : EndLatex
const double sigma = std::stod(argv[6]);
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The SigmoidImageFilter requires two parameters that define the linear
// transformation to be applied to the sigmoid argument. These parameters
// have been discussed in Sections~\ref{sec:IntensityNonLinearMapping} and
// \ref{sec:FastMarchingImageFilter}.
//
// Software Guide : EndLatex
const double alpha = std::stod(argv[7]);
const double beta = std::stod(argv[8]);
// Software Guide : BeginCodeSnippet
sigmoid->SetAlpha(alpha);
sigmoid->SetBeta(beta);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The FastMarchingImageFilter requires the user to provide a seed
// point from which the level set will be generated. The user can actually
// pass not only one seed point but a set of them. Note the
// FastMarchingImageFilter is used here only as a helper in the
// determination of an initial level set. We could have used the
// \doxygen{DanielssonDistanceMapImageFilter} in the same way.
//
// \index{itk::FastMarchingImageFilter!Multiple seeds}
//
// The seeds are stored in a container. The type of this
// container is defined as \code{NodeContainer} among the
// FastMarchingImageFilter traits.
//
// \index{itk::FastMarchingImageFilter!Nodes}
// \index{itk::FastMarchingImageFilter!NodeContainer}
// \index{itk::FastMarchingImageFilter!NodeType}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using NodeContainer = FastMarchingFilterType::NodeContainer;
using NodeType = FastMarchingFilterType::NodeType;
NodeContainer::Pointer seeds = NodeContainer::New();
// Software Guide : EndCodeSnippet
seedPosition[0] = std::stoi(argv[3]);
seedPosition[1] = std::stoi(argv[4]);
// Software Guide : BeginLatex
//
// Nodes are created as stack variables and initialized with a value and
// an \doxygen{Index} position. Note that we assign the negative of the
// value of the user-provided distance to the unique node of the seeds
// passed to the FastMarchingImageFilter. In this way, the value
// will increment as the front is propagated, until it reaches the zero
// value corresponding to the contour. After this, the front will continue
// propagating until it fills up the entire image. The initial distance is
// taken from the command line arguments. The rule of thumb for the user
// is to select this value as the distance from the seed points at which
// the initial contour should be.
//
// \index{itk::FastMarchingImageFilter!Seed initialization}
//
// Software Guide : EndLatex
const double initialDistance = std::stod(argv[5]);
// Software Guide : BeginCodeSnippet
NodeType node;
const double seedValue = -initialDistance;
node.SetValue(seedValue);
node.SetIndex(seedPosition);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The list of nodes is initialized and then every node is inserted using
// \code{InsertElement()}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
seeds->Initialize();
seeds->InsertElement(0, node);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The set of seed nodes is now passed to the FastMarchingImageFilter with
// the method \code{SetTrialPoints()}.
//
// \index{itk::FastMarchingImageFilter!SetTrialPoints()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
fastMarching->SetTrialPoints(seeds);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Since the FastMarchingImageFilter is used here only as a distance map
// generator, it does not require a speed image as input. Instead, the
// constant value $1.0$ is passed using the \code{SetSpeedConstant()}
// method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
fastMarching->SetSpeedConstant(1.0);
// Software Guide : EndCodeSnippet
// Here we configure all the writers required to see the intermediate
// outputs of the pipeline. This is added here only for
// pedagogical/debugging purposes. These intermediate output are normaly
// not required. Only the output of the final thresholding filter should
// be relevant. Observing intermediate output is helpful in the process
// of fine tuning the parameters of filters in the pipeline.
//
CastFilterType::Pointer caster1 = CastFilterType::New();
CastFilterType::Pointer caster2 = CastFilterType::New();
CastFilterType::Pointer caster3 = CastFilterType::New();
CastFilterType::Pointer caster4 = CastFilterType::New();
WriterType::Pointer writer1 = WriterType::New();
WriterType::Pointer writer2 = WriterType::New();
WriterType::Pointer writer3 = WriterType::New();
WriterType::Pointer writer4 = WriterType::New();
const std::string outputImageFilePrefix =
itksys::SystemTools::GetFilenameWithoutExtension(outputImageFile);
caster1->SetInput(smoothing->GetOutput());
writer1->SetInput(caster1->GetOutput());
writer1->SetFileName(outputImageFilePrefix + "Smoothing.png");
caster1->SetOutputMinimum(0);
caster1->SetOutputMaximum(255);
writer1->Update();
writer2->SetInput(caster2->GetOutput());
caster2->SetOutputMinimum(0);
caster2->SetOutputMaximum(255);
writer2->Update();
caster3->SetInput(sigmoid->GetOutput());
writer3->SetInput(caster3->GetOutput());
writer3->SetFileName(outputImageFilePrefix + "Sigmoid.png");
caster3->SetOutputMinimum(0);
caster3->SetOutputMaximum(255);
writer3->Update();
caster4->SetInput(fastMarching->GetOutput());
writer4->SetInput(caster4->GetOutput());
writer4->SetFileName(outputImageFilePrefix + "FastMarching.png");
caster4->SetOutputMinimum(0);
caster4->SetOutputMaximum(255);
// Software Guide : BeginLatex
//
// The FastMarchingImageFilter requires the user to specify the
// size of the image to be produced as output. This is done using the
// \code{SetOutputSize()}. Note that the size is obtained here from the
// output image of the smoothing filter. The size of this image is valid
// only after the \code{Update()} methods of this filter have been called
// directly or indirectly.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
fastMarching->SetOutputSize(
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// ShapeDetectionLevelSetImageFilter provides two parameters to control
// the competition between the propagation or expansion term and the
// curvature smoothing term. The methods \code{SetPropagationScaling()}
// and \code{SetCurvatureScaling()} defines the relative weighting between
// the two terms. In this example, we will set the propagation scaling to
// one and let the curvature scaling be an input argument. The larger the
// the curvature scaling parameter the smoother the resulting
// segmentation. However, the curvature scaling parameter should not be
// set too large, as it will draw the contour away from the shape
// boundaries.
//
// \index{itk::Shape\-Detection\-LevelSet\-Image\-Filter!SetPropagationScaling()}
// \index{itk::Segmentation\-Level\-Set\-Image\-Filter!SetPropagationScaling()}
// \index{itk::Shape\-Detection\-Level\-Set\-Image\-Filter!SetCurvatureScaling()}
// \index{itk::Segmentation\-Level\-Set\-Image\-Filter!SetCurvatureScaling()}
//
// Software Guide : EndLatex
const double curvatureScaling = std::stod(argv[9]);
const double propagationScaling = std::stod(argv[10]);
// Software Guide : BeginCodeSnippet
shapeDetection->SetPropagationScaling(propagationScaling);
shapeDetection->SetCurvatureScaling(curvatureScaling);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Once activated, the level set evolution will stop if the convergence
// criteria or the maximum number of iterations is reached. The
// convergence criteria are defined in terms of the root mean squared
// (RMS) change in the level set function. The evolution is said to have
// converged if the RMS change is below a user-specified threshold. In a
// real application, it is desirable to couple the evolution of the zero
// set to a visualization module, allowing the user to follow the
// evolution of the zero set. With this feedback, the user may decide when
// to stop the algorithm before the zero set leaks through the regions of
// low gradient in the contour of the anatomical structure to be
// segmented.
//
// \index{itk::Shape\-Detection\-Level\-Set\-Image\-Filter!SetMaximumRMSError()}
// \index{itk::Shape\-Detection\-Level\-Set\-Image\-Filter!SetNumberOfIterations()}
// \index{itk::Segmentation\-Level\-Set\-Image\-Filter!SetMaximumRMSError()}
// \index{itk::Segmentation\-Level\-Set\-Image\-Filter!SetNumberOfIterations()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
shapeDetection->SetMaximumRMSError(0.02);
shapeDetection->SetNumberOfIterations(800);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The invocation of the \code{Update()} method on the writer triggers the
// execution of the pipeline. As usual, the call is placed in a
// \code{try/catch} block should any errors occur or exceptions be thrown.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
writer->Update();
}
catch (const itk::ExceptionObject & excep)
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Print out some useful information
std::cout << std::endl;
std::cout << "Max. no. iterations: "
<< shapeDetection->GetNumberOfIterations() << std::endl;
std::cout << "Max. RMS error: " << shapeDetection->GetMaximumRMSError()
<< std::endl;
std::cout << std::endl;
std::cout << "No. elpased iterations: "
<< shapeDetection->GetElapsedIterations() << std::endl;
std::cout << "RMS change: " << shapeDetection->GetRMSChange() << std::endl;
writer4->Update();
// The following writer type is used to save the output of the time-crossing
// map in a file with apropiate pixel representation. The advantage of
// saving this image in native format is that it can be used with a viewer
// to help determine an appropriate threshold to be used on the output of
// the fastmarching filter.
//
using InternalWriterType = itk::ImageFileWriter<InternalImageType>;
InternalWriterType::Pointer mapWriter = InternalWriterType::New();
mapWriter->SetInput(fastMarching->GetOutput());
mapWriter->SetFileName("ShapeDetectionLevelSetFilterOutput4.mha");
mapWriter->Update();
InternalWriterType::Pointer speedWriter = InternalWriterType::New();
speedWriter->SetInput(sigmoid->GetOutput());
speedWriter->SetFileName("ShapeDetectionLevelSetFilterOutput3.mha");
speedWriter->Update();
// Software Guide : BeginLatex
//
// Let's now run this example using as input the image
// \code{BrainProtonDensitySlice.png} provided in the directory
// \code{Examples/Data}. We can easily segment the major anatomical
// structures by providing seeds in the appropriate locations.
// Table~\ref{tab:ShapeDetectionLevelSetFilterOutput} presents the
// parameters used for some structures. For all of the examples
// illustrated in this table, the propagation scaling was set to $1.0$, and
// the curvature scaling set to 0.05.
//
// \begin{table}
// \begin{center}
// \begin{tabular}{|l|c|c|c|c|c|c|}
// \hline
// Structure & Seed Index & Distance & $\sigma$ & $\alpha$ & $\beta$ &
// Output Image \\ \hline Left Ventricle & $(81,114)$ & 5.0 & 1.0 & -0.5
// & 3.0 & First in Figure \ref{fig:ShapeDetectionLevelSetFilterOutput2}
// \\ \hline Right Ventricle &
// $(99,114)$ & 5.0 & 1.0 & -0.5 & 3.0 & Second in Figure
// \ref{fig:ShapeDetectionLevelSetFilterOutput2} \\ \hline White matter &
// $(56, 92)$ & 5.0 & 1.0 & -0.3 & 2.0 & Third in Figure
// \ref{fig:ShapeDetectionLevelSetFilterOutput2} \\ \hline Gray matter &
// $(40, 90)$ & 5.0 & 0.5 & -0.3 & 2.0 & Fourth in Figure
// \ref{fig:ShapeDetectionLevelSetFilterOutput2} \\ \hline \end{tabular}
// \end{center}
// \itkcaption[ShapeDetection example parameters]{Parameters used for
// segmenting some brain structures shown in
// Figure~\ref{fig:ShapeDetectionLevelSetFilterOutput} using the filter
// ShapeDetectionLevelSetFilter. All of them used a propagation
// scaling of $1.0$ and curvature scaling of
// $0.05$.\label{tab:ShapeDetectionLevelSetFilterOutput}}
// \end{table}
//
// Figure~\ref{fig:ShapeDetectionLevelSetFilterOutput} presents the
// intermediate outputs of the pipeline illustrated in
// Figure~\ref{fig:ShapeDetectionCollaborationDiagram}. They are from left
// to right: the output of the anisotropic diffusion filter, the gradient
// magnitude of the smoothed image and the sigmoid of the gradient
// magnitude which is finally used as the edge potential for the
// ShapeDetectionLevelSetImageFilter.
//
// Notice that in Figure~\ref{fig:ShapeDetectionLevelSetFilterOutput2} the
// segmented shapes are rounder than in
// Figure~\ref{fig:FastMarchingImageFilterOutput2} due to the effects of
// the curvature term in the driving equation. As with the previous
// example, segmentation of the gray matter is still problematic.
//
// \begin{figure} \center
// \includegraphics[height=0.40\textheight]{BrainProtonDensitySlice}
// \includegraphics[height=0.40\textheight]{ShapeDetectionLevelSetFilterOutput1Smoothing}
// \includegraphics[height=0.40\textheight]{ShapeDetectionLevelSetFilterOutput1Sigmoid}
// \itkcaption[ShapeDetectionLevelSetImageFilter intermediate output]{Images
// generated by the segmentation process based on the
// ShapeDetectionLevelSetImageFilter. From left to right and top to bottom:
// input image to be segmented, image smoothed with an edge-preserving
// smoothing filter, gradient magnitude of the smoothed image, sigmoid of
// the gradient magnitude. This last image, the sigmoid, is used to compute
// the speed term for the front propagation.}
// \label{fig:ShapeDetectionLevelSetFilterOutput}
// \end{figure}
//
// A larger number of iterations is reguired for segmenting large
// structures since it takes longer for the front to propagate and cover
// the structure. This drawback can be easily mitigated by setting many
// seed points in the initialization of the
// FastMarchingImageFilter. This will generate an initial level
// set much closer in shape to the object to be segmented and hence
// require fewer iterations to fill and reach the edges of the anatomical
// structure.
//
// \begin{figure} \center
// \includegraphics[width=0.24\textwidth]{ShapeDetectionLevelSetFilterOutput1}
// \includegraphics[width=0.24\textwidth]{ShapeDetectionLevelSetFilterOutput2}
// \includegraphics[width=0.24\textwidth]{ShapeDetectionLevelSetFilterOutput3}
// \includegraphics[width=0.24\textwidth]{ShapeDetectionLevelSetFilterOutput4}
// \itkcaption[ShapeDetectionLevelSetImageFilter segmentations]{Images
// generated by the segmentation process based on the
// ShapeDetectionLevelSetImageFilter. From left to right: segmentation of
// the left ventricle, segmentation of the right ventricle, segmentation of
// the white matter, attempt of segmentation of the gray matter.}
// \label{fig:ShapeDetectionLevelSetFilterOutput2}
// \end{figure}
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itkSigmoidImageFilter.h
itk::BinaryThresholdImageFilter
Binarize an input image by thresholding.
Definition: itkBinaryThresholdImageFilter.h:139
itk::SigmoidImageFilter
Computes the sigmoid function pixel-wise.
Definition: itkSigmoidImageFilter.h:150
itkFastMarchingImageFilter.h
Data source that reads image data from a single file.
itk::GTest::TypedefsAndConstructors::Dimension2::IndexType
ImageBaseType::IndexType IndexType
Definition: itkGTestTypedefsAndConstructors.h:50
itk::CurvatureAnisotropicDiffusionImageFilter
This filter performs anisotropic diffusion on a scalar itk::Image using the modified curvature diffus...
itkCurvatureAnisotropicDiffusionImageFilter.h
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::FastMarchingImageFilter
Solve an Eikonal equation using Fast Marching.
Definition: itkFastMarchingImageFilter.h:136
Computes the Magnitude of the Gradient of an image by convolution with the first derivative of a Gaus...
itkRescaleIntensityImageFilter.h
itkShapeDetectionLevelSetImageFilter.h
itkImageFileWriter.h
itk::ShapeDetectionLevelSetImageFilter
Segments structures in images based on a user supplied edge potential map.
Definition: itkShapeDetectionLevelSetImageFilter.h:106
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
itkBinaryThresholdImageFilter.h
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44