ITK  5.4.0 Insight Toolkit
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// Software Guide : BeginLatex
//
// $N$-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
//
// 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.
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
//
//
// 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 << "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
// 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.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
VectorImageType>;
auto filter = FilterType::New();
// Software Guide : EndCodeSnippet
VectorImageType>;
// 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
// 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()}.
//
//
// 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>;
auto component = ComponentFilterType::New();
// Select the component to extract.
component->SetIndex(0);
using WritePixelType = unsigned char;
using WriteImageType = itk::Image<WritePixelType, 2>;
using RescaleFilterType =
auto rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
auto writer = WriterType::New();
rescaler->SetInput(component->GetOutput());
writer->SetInput(rescaler->GetOutput());
// Save the component of the original gradient
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
// of the VectorGradientAnisotropicDiffusionImageFilter on the $X$ component
// of the gradient from a MRI proton density brain image.}
// \end{figure}
//
// Figure
// 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
Data source that reads image data from a single file.
itk::VectorIndexSelectionCastImageFilter
Extracts the selected index of the vector that is the input pixel type.
Definition: itkVectorIndexSelectionCastImageFilter.h:88
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:88
Computes the gradient of an image by convolution with the first derivative of a Gaussian.