ITK  5.2.0
Insight Toolkit
Examples/Filtering/GradientVectorFlowImageFilter.cxx
/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
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*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {GradientRecursiveGaussianImageFilterTest.mha}
// ARGUMENTS: {GradientVectorFlowImageFilterOutput.mha}
// ARGUMENTS: 5 2000.0
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The \doxygen{GradientVectorFlowImageFilter} smooths multi-components
// images such as vector fields and color images by applying a computation of
// the diffusion equation. A typical use of this filter is to smooth the
// vector field resulting from computing the gradient of an image, with the
// purpose of using the smoothed field in order to guide a deformable model.
//
// The input image must be a multi-components images.
//
// \index{itk::GradientVectorFlowImageFilter}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
//
// \index{itk::GradientVectorFlowImageFilter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int
main(int argc, char * argv[])
{
if (argc < 5)
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile";
std::cerr << " numberOfIterations noiseLevel" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be selected based on the pixel types required for the input
// and output images. In this particular case, the input and output pixel
// types are multicomponents type such as itk::Vectors.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr unsigned int Dimension = 3;
using InputValueType = float;
using OutputValueType = float;
using OutputPixelType = itk::Vector<OutputValueType, Dimension>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// With them, the input and output image types can be instantiated.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputImageType = itk::Image<InputPixelType, Dimension>;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The GradientVectorFlow filter type is now instantiated using both the
// input image and the output image types.
//
// \index{itk::GradientVectorFlowImageFilter!instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(argv[1]);
// Software Guide : BeginLatex
//
// A filter object is created by invoking the \code{New()} method and
// assigning the result to a \doxygen{SmartPointer}.
//
// \index{itk::GradientVectorFlowImageFilter!New()}
// \index{itk::GradientVectorFlowImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input image can be obtained from the output of another filter. Here,
// an image reader is used as source.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetInput(reader->GetOutput());
// Software Guide : EndCodeSnippet
const unsigned int numberOfIterations = std::stoi(argv[3]);
const double noiseLevel = std::stod(argv[4]);
// Software Guide : BeginLatex
//
// The GradientVectorFlow filter requires two parameters, the number of
// iterations to be performed and the noise level of the input image. The
// noise level will be used to estimate the time step that should be used
// in the computation of the diffusion. These two parameters are set using
// the methods \code{SetNumberOfIterations()} and \code{SetNoiseLevel()}
// respectively. Then the filter can be executed by invoking
// \code{Update()}.
//
// \index{itk::GradientVectorFlowImageFilter!Update()}
// \index{itk::GradientVectorFlowImageFilter!SetNoiseLevel()}
// \index{itk::GradientVectorFlowImageFilter!SetNumberOfIterations()}
// \index{SetNoiseLevel()!itk::GradientVectorFlowImageFilter}
// \index{SetNumberOfIterations()!itk::GradientVectorFlowImageFilter}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetIterationNum(numberOfIterations);
filter->SetNoiseLevel(noiseLevel);
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// When using as input the result of a gradient filter, then the typical
// values for the noise level will be around 2000.0.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// If the output of this filter has been connected to other filters down
// the pipeline, updating any of the downstream filters will
// triggered the execution of this one. For example, a writer filter could
// have been used after the curvature flow filter.
//
// Software Guide : EndLatex
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv[2]);
// Software Guide : BeginCodeSnippet
writer->SetInput(filter->GetOutput());
writer->Update();
// Software Guide : EndCodeSnippet
// In order to visualize the resulting vector field you could use ParaView
// or VV (the 4D Slicer).
return EXIT_SUCCESS;
}
itk::Vector
A templated class holding a n-Dimensional vector.
Definition: itkVector.h:62
itkImageFileReader.h
itk::GradientVectorFlowImageFilter
This class computes a diffusion of the gradient vectors for graylevel or binary edge map derive from ...
Definition: itkGradientVectorFlowImageFilter.h:50
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
itkRescaleIntensityImageFilter.h
itkImageFileWriter.h
itkGradientVectorFlowImageFilter.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