ITK  5.2.0
Insight Toolkit
Examples/Filtering/VectorGradientAnisotropicDiffusionImageFilter.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
* limitations under the License.
*
*=========================================================================*/
// Software Guide : BeginLatex
//
// The \doxygen{VectorGradientAnisotropicDiffusionImageFilter} implements an
// $N$-dimensional version of the classic Perona-Malik anisotropic diffusion
// equation for vector-valued images. Typically in vector-valued diffusion,
// vector components are diffused independently of one another using a
// conductance term that is linked across the components. The diffusion
// equation was illustrated in
// \ref{sec:GradientAnisotropicDiffusionImageFilter}.
//
// This filter is designed to process images of \doxygen{Vector} type. The
// code relies on various type alias and overloaded operators defined in
// \doxygen{Vector}. It is perfectly reasonable, however, to apply this
// filter to images of other, user-defined types as long as the appropriate
// type alias and operator overloads are in place. As a general rule, follow
// the example of \doxygen{Vector} in defining your data types.
//
// \index{itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
//
// \index{itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int
main(int argc, char * argv[])
{
if (argc < 6)
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputGradientImageFile ";
std::cerr << "outputSmoothedGradientImageFile ";
std::cerr << "numberOfIterations timeStep" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be selected based on required pixel type for the input and
// output images. The image types are defined using the pixel type and
// the dimension.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputPixelType = float;
using VectorPixelType = itk::CovariantVector<float, 2>;
using InputImageType = itk::Image<InputPixelType, 2>;
using VectorImageType = itk::Image<VectorPixelType, 2>;
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(argv[1]);
// Software Guide : BeginLatex
//
// The filter type is now instantiated using both the input image and the
// output image types. The filter object is created by the \code{New()}
// method.
//
// \index{itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter!instantiation}
// \index{itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter!New()}
// \index{itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
VectorImageType>;
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
using GradientFilterType =
VectorImageType>;
GradientFilterType::Pointer gradient = GradientFilterType::New();
// Software Guide : BeginLatex
//
// The input image can be obtained from the output of another filter. Here,
// an image reader is used as source and its data is passed through a
// gradient filter in order to generate an image of vectors.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
gradient->SetInput(reader->GetOutput());
filter->SetInput(gradient->GetOutput());
// Software Guide : EndCodeSnippet
const unsigned int numberOfIterations = std::stoi(argv[4]);
const double timeStep = std::stod(argv[5]);
// Software Guide : BeginLatex
//
// This filter requires two parameters: the number of iterations to be
// performed and the time step used in the computation of the level set
// evolution. These parameters are set using the methods
// \code{SetNumberOfIterations()} and \code{SetTimeStep()} respectively.
// The filter can be executed by invoking \code{Update()}.
//
// \index{itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter!Update()}
// \index{itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter!SetTimeStep()}
// \index{itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter!SetNumberOfIterations()}
// \index{SetTimeStep()!itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter}
// \index{SetNumberOfIterations()!itk::Vector\-Gradient\-Anisotropic\-Diffusion\-Image\-Filter}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetNumberOfIterations(numberOfIterations);
filter->SetTimeStep(timeStep);
filter->SetConductanceParameter(1.0);
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Typical values for the time step are $0.125$ in $2D$ images and
// $0.0625$ in $3D$ images. The number of iterations can be usually around
// $5$, however more iterations will result in further smoothing and will
// linearly increase the computing time.
//
// Software Guide : EndLatex
// If the output of this filter has been connected to other filters down
// the pipeline, updating any of the downstream filters would have
// triggered the execution of this one. For example, a writer filter could
// have been used after the curvature flow filter.
//
using OutputPixelType = float;
using OutputImageType = itk::Image<OutputPixelType, 2>;
using ComponentFilterType =
OutputImageType>;
ComponentFilterType::Pointer component = ComponentFilterType::New();
// Select the component to extract.
component->SetIndex(0);
using WritePixelType = unsigned char;
using WriteImageType = itk::Image<WritePixelType, 2>;
using RescaleFilterType =
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
WriterType::Pointer writer = WriterType::New();
rescaler->SetInput(component->GetOutput());
writer->SetInput(rescaler->GetOutput());
// Save the component of the original gradient
component->SetInput(gradient->GetOutput());
writer->SetFileName(argv[2]);
writer->Update();
// Save the component of the smoothed gradient
component->SetInput(filter->GetOutput());
writer->SetFileName(argv[3]);
writer->Update();
// Software Guide : BeginLatex
//
// \begin{figure} \center
// \includegraphics[width=0.44\textwidth]{VectorGradientAnisotropicDiffusionImageFilterInput}
// \includegraphics[width=0.44\textwidth]{VectorGradientAnisotropicDiffusionImageFilterOutput}
// \itkcaption[VectorGradientAnisotropicDiffusionImageFilter output]{Effect
// of the VectorGradientAnisotropicDiffusionImageFilter on the $X$ component
// of the gradient from a MRI proton density brain image.}
// \label{fig:VectorGradientAnisotropicDiffusionImageFilterInputOutput}
// \end{figure}
//
// Figure
// \ref{fig:VectorGradientAnisotropicDiffusionImageFilterInputOutput}
// illustrates the effect of this filter on a MRI proton density image of
// the brain. The images show the $X$ component of the gradient before
// (left) and after (right) the application of the filter. In this example
// the filter was run with a time step of $0.25$, and $5$ iterations.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itkVectorIndexSelectionCastImageFilter.h
itkImageFileReader.h
itkVectorGradientAnisotropicDiffusionImageFilter.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::VectorIndexSelectionCastImageFilter
Extracts the selected index of the vector that is the input pixel type.
Definition: itkVectorIndexSelectionCastImageFilter.h:96
itkGradientRecursiveGaussianImageFilter.h
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::GradientRecursiveGaussianImageFilter
Computes the gradient of an image by convolution with the first derivative of a Gaussian.
Definition: itkGradientRecursiveGaussianImageFilter.h:59
itk::VectorGradientAnisotropicDiffusionImageFilter
Definition: itkVectorGradientAnisotropicDiffusionImageFilter.h:61
itkRescaleIntensityImageFilter.h
itkImageFileWriter.h
itk::CovariantVector
A templated class holding a n-Dimensional covariant vector.
Definition: itkCovariantVector.h:70
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86