ITK  5.2.0
Insight Toolkit
Examples/Segmentation/CannySegmentationLevelSetImageFilter.cxx
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*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// INPUTS: {ThresholdSegmentationLevelSetImageFilterVentricle.png}
// OUTPUTS: {CannySegmentationLevelSetImageFilterVentricle1.png}
// ARGUMENTS: 7.0 0.1 -10.0 127.5 15
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// \index{itk::Canny\-Segmentation\-LevelSet\-Image\-Filter}
//
// The \doxygen{CannySegmentationLevelSetImageFilter} defines a speed term
// that minimizes distance to the Canny edges in an image. The initial
// level set model moves through a gradient advection field until it locks
// onto those edges. This filter is more suitable for refining existing
// segmentations than as a region-growing algorithm.
//
// The two terms defined for the CannySegmentationLevelSetImageFilter
// are the advection term and the propagation term from
// Equation~\ref{eqn:LevelSetEquation}. The advection term is constructed by
// minimizing the squared distance transform from the Canny edges.
//
// \begin{equation}
// \label{eqn:CannySegmentationLevelSetImageFilterAdvection}
// \mbox{min} \int D^2 \Rightarrow D \nabla D
// \end{equation}
//
// where the distance transform $D$ is calculated using a
// \doxygen{DanielssonDistanceMapImageFilter} applied to the output of the
// \doxygen{CannyEdgeDetectionImageFilter}.
//
// For cases in which some surface expansion is to be allowed, a non-zero
// value may be set for the propagation term. The propagation term is simply
// $D$. As with all ITK level set segmentation filters, the curvature term
// controls the smoothness of the surface.
//
// CannySegmentationLevelSetImageFilter expects two inputs. The first is an
// initial level set in the form of an \doxygen{Image}. The second input is
// the feature image $g$ from which propagation and advection terms are
// calculated. It is generally a good idea to do some preprocessing of the
// feature image to remove noise.
//
// Figure~\ref{fig:CannySegmentationLevelSetImageFilterDiagram} shows how the
// image processing pipeline is constructed. We read two images: the image to
// segment and the image that contains the initial implicit surface. The goal
// is to refine the initial model from the second input and not to grow a new
// segmentation from seed points. The \code{feature} image is preprocessed
// with a few iterations of an anisotropic diffusion filter.
//
// \begin{figure} \center
// \includegraphics[width=0.9\textwidth]{CannySegmentationLevelSetImageFilterCollaborationDiagram1}
// \itkcaption[CannySegmentationLevelSetImageFilter collaboration
// diagram]{Collaboration diagram for the CannySegmentationLevelSetImageFilter
// applied to a segmentation task.}
// \label{fig:CannySegmentationLevelSetImageFilterDiagram}
// \end{figure}
//
// Let's start by including the appropriate header file.
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int
main(int argc, char * argv[])
{
if (argc < 9)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " InputImage InitialModel OutputImage";
std::cerr << " CannyThreshold ";
std::cerr << " CannyVariance ";
std::cerr << " AdvectionWeight";
std::cerr << " InitialModelIsovalue";
std::cerr << " MaximumIterations";
std::cerr << " [OutputSpeedImage]" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// We define the image type using a particular pixel type and
// dimension. In this case we will use 2D \code{float} images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InternalPixelType = float;
constexpr unsigned int Dimension = 2;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
// Software Guide : EndCodeSnippet
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using ThresholdingFilterType =
ThresholdingFilterType::Pointer thresholder = ThresholdingFilterType::New();
thresholder->SetUpperThreshold(10.0);
thresholder->SetLowerThreshold(0.0);
thresholder->SetOutsideValue(0);
thresholder->SetInsideValue(255);
ReaderType::Pointer reader1 = ReaderType::New();
ReaderType::Pointer reader2 = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
reader1->SetFileName(argv[1]);
reader2->SetFileName(argv[2]);
writer->SetFileName(argv[3]);
// Software Guide : BeginLatex
//
// The input image will be processed with a few iterations of
// feature-preserving diffusion. We create a filter and set the
// appropriate parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using DiffusionFilterType =
InternalImageType>;
DiffusionFilterType::Pointer diffusion = DiffusionFilterType::New();
diffusion->SetNumberOfIterations(5);
diffusion->SetTimeStep(0.125);
diffusion->SetConductanceParameter(1.0);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The following lines define and instantiate a
// CannySegmentationLevelSetImageFilter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using CannySegmentationLevelSetImageFilterType =
InternalImageType>;
CannySegmentationLevelSetImageFilterType::Pointer cannySegmentation =
CannySegmentationLevelSetImageFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As with the other ITK level set segmentation filters, the terms of the
// CannySegmentationLevelSetImageFilter level set equation can be
// weighted by scalars. For this application we will modify the relative
// weight of the advection term. The propagation and curvature term weights
// are set to their defaults of $0$ and $1$, respectively.
//
// \index{itk::Canny\-Segmentation\-LevelSet\-Image\-Filter!SetAdvectionScaling()}
// \index{itk::Segmentation\-LevelSet\-ImageFilter!SetAdvectionScaling()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
cannySegmentation->SetAdvectionScaling(::std::stod(argv[6]));
cannySegmentation->SetCurvatureScaling(1.0);
cannySegmentation->SetPropagationScaling(0.0);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The maximum number of iterations is specified from the command line.
// It may not be desirable in some applications to run the filter to
// convergence. Only a few iterations may be required.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
cannySegmentation->SetMaximumRMSError(0.01);
cannySegmentation->SetNumberOfIterations(::std::stoi(argv[8]));
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// There are two important parameters in the
// CannySegmentationLevelSetImageFilter to control the behavior of the
// Canny edge detection. The \emph{variance} parameter controls the
// amount of Gaussian smoothing on the input image. The \emph{threshold}
// parameter indicates the lowest allowed value in the output image.
// Thresholding is used to suppress Canny edges whose gradient magnitudes
// fall below a certain value.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
cannySegmentation->SetThreshold(::std::stod(argv[4]));
cannySegmentation->SetVariance(::std::stod(argv[5]));
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, it is very important to specify the isovalue of the surface in
// the initial model input image. In a binary image, for example, the
// isosurface is found midway between the foreground and background values.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
cannySegmentation->SetIsoSurfaceValue(::std::stod(argv[7]));
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filters are now connected in a pipeline indicated in
// Figure~\ref{fig:CannySegmentationLevelSetImageFilterDiagram}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
diffusion->SetInput(reader1->GetOutput());
cannySegmentation->SetInput(reader2->GetOutput());
cannySegmentation->SetFeatureImage(diffusion->GetOutput());
thresholder->SetInput(cannySegmentation->GetOutput());
writer->SetInput(thresholder->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Invoking 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 to handle any exceptions that may 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: "
<< cannySegmentation->GetNumberOfIterations() << std::endl;
std::cout << "Max. RMS error: " << cannySegmentation->GetMaximumRMSError()
<< std::endl;
std::cout << std::endl;
std::cout << "No. elpased iterations: "
<< cannySegmentation->GetElapsedIterations() << std::endl;
std::cout << "RMS change: " << cannySegmentation->GetRMSChange()
<< std::endl;
// Software Guide : BeginLatex
//
// We can use this filter to make some subtle refinements to the ventricle
// segmentation from the previous example that used the
// \doxygen{ThresholdSegmentationLevelSetImageFilter}. The application
// was run using \code{Examples/Data/BrainProtonDensitySlice.png} and
// \code{Examples/Data/VentricleModel.png} as inputs, a \code{threshold}
// of $7.0$, \code{variance} of $0.1$, \code{advection weight} of $10.0$,
// and an initial isosurface value of $127.5$. One case was run for $15$
// iterations and the second was run to convergence. Compare the results
// in the two rightmost images of
// Figure~\ref{fig:CannySegmentationLevelSetImageFilter} with the
// ventricle segmentation from
// Figure~\ref{fig:ThresholdSegmentationLevelSetImageFilter} shown in the
// middle. Jagged edges are straightened and the small spur at the upper
// right-hand side of the mask has been removed.
//
// \begin{figure}
// \includegraphics[width=0.24\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.24\textwidth]{ThresholdSegmentationLevelSetImageFilterVentricle}
// \includegraphics[width=0.24\textwidth]{CannySegmentationLevelSetImageFilterVentricle1}
// \includegraphics[width=0.24\textwidth]{CannySegmentationLevelSetImageFilterVentricle2}
// \itkcaption[Segmentation results of CannyLevelSetImageFilter]{Results of
// applying the CannySegmentationLevelSetImageFilter to a prior ventricle
// segmentation. Shown from left to right are the original image, the
// prior segmentation of the ventricle from
// Figure~\ref{fig:ThresholdSegmentationLevelSetImageFilter}, $15$
// iterations of the CannySegmentationLevelSetImageFilter, and the
// CannySegmentationLevelSetImageFilter run to convergence.}
// \label{fig:CannySegmentationLevelSetImageFilter}
// \end{figure}
//
// The free parameters of this filter can be adjusted to achieve a wide
// range of shape variations from the original model. Finding the right
// parameters for your particular application is usually a process of
// trial and error. As with most ITK level set segmentation filters,
// examining the propagation (speed) and advection images can help the
// process of tuning parameters. These images are available using
// \code{Set/Get} methods from the filter after it has been updated.
//
// Software Guide : EndLatex
if (argc > 9)
{
const char * speedImageFileName = argv[9];
// Software Guide : BeginLatex
//
// In some cases it is interesting to take a direct look at the speed
// image used internally by this filter. This may help for setting the
// correct parameters for driving the segmentation. In order to obtain
// such speed image, the method \code{GenerateSpeedImage()} should be
// invoked first. Then we can recover the speed image with the
// \code{GetSpeedImage()} method as illustrated in the following lines.
//
// \index{itk::Canny\-Segmentation\-LevelSet\-Image\-Filter!GenerateSpeedImage()}
// \index{itk::Segmentation\-LevelSet\-ImageFilter!GenerateSpeedImage()}
// \index{itk::Canny\-Segmentation\-LevelSet\-Image\-Filter!GetSpeedImage()}
// \index{itk::Segmentation\-LevelSet\-ImageFilter!GetSpeedImage()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
cannySegmentation->GenerateSpeedImage();
using SpeedImageType =
CannySegmentationLevelSetImageFilterType::SpeedImageType;
using SpeedWriterType = itk::ImageFileWriter<SpeedImageType>;
SpeedWriterType::Pointer speedWriter = SpeedWriterType::New();
speedWriter->SetInput(cannySegmentation->GetSpeedImage());
// Software Guide : EndCodeSnippet
speedWriter->SetFileName(speedImageFileName);
try
{
speedWriter->Update();
}
catch (const itk::ExceptionObject & excep)
{
std::cerr << "Exception caught ! while writing the speed image"
<< std::endl;
std::cerr << "Filename : " << speedImageFileName << std::endl;
std::cerr << excep << std::endl;
return EXIT_FAILURE;
}
}
return EXIT_SUCCESS;
}
itkGradientAnisotropicDiffusionImageFilter.h
itkCannySegmentationLevelSetImageFilter.h
itk::BinaryThresholdImageFilter
Binarize an input image by thresholding.
Definition: itkBinaryThresholdImageFilter.h:139
itk::CannySegmentationLevelSetImageFilter
Segments structures in images based on image features derived from pseudo-canny-edges.
Definition: itkCannySegmentationLevelSetImageFilter.h:130
itkImageFileReader.h
itkImage.h
itkFastMarchingImageFilter.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkImageFileWriter.h
itkZeroCrossingImageFilter.h
itkBinaryThresholdImageFilter.h
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::GradientAnisotropicDiffusionImageFilter
This filter performs anisotropic diffusion on a scalar itk::Image using the classic Perona-Malik,...
Definition: itkGradientAnisotropicDiffusionImageFilter.h:51
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44