ITK  5.2.0 Insight Toolkit
Examples/Filtering/DiscreteGaussianImageFilter.cxx
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {DiscreteGaussianImageFilterOutput.png}
// ARGUMENTS: 4 9
// Software Guide : EndCommandLineArgs
//
// Software Guide : BeginLatex
//
// \begin{floatingfigure}[rlp]{6cm}
// \centering
// \includegraphics[width=6cm]{DiscreteGaussian}
// \caption[DiscreteGaussianImageFilter Gaussian diagram.]
// {Discretized Gaussian.\label{fig:DiscretizedGaussian}}
// \end{floatingfigure}
//
// The \doxygen{DiscreteGaussianImageFilter} computes the convolution of the
// input image with a Gaussian kernel. This is done in $ND$ by taking
// advantage of the separability of the Gaussian kernel. A one-dimensional
// Gaussian function is discretized on a convolution kernel. The size of the
// kernel is extended until there are enough discrete points in the Gaussian
// to ensure that a user-provided maximum error is not exceeded. Since the
// size of the kernel is unknown a priori, it is necessary to impose a limit
// to its growth. The user can thus provide a value to be the maximum
// admissible size of the kernel. Discretization error is defined as the
// difference between the area under the discrete Gaussian curve (which has
// finite support) and the area under the continuous Gaussian.
//
// Gaussian kernels in ITK are constructed according to the theory of Tony
// Lindeberg \cite{Lindeberg1994} so that smoothing and derivative operations
// commute before and after discretization. In other words, finite
// difference derivatives on an image $I$ that has been smoothed by
// convolution with the Gaussian are equivalent to finite differences
// computed on $I$ by convolving with a derivative of the Gaussian.
//
// \index{itk::DiscreteGaussianImageFilter}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
// As with other examples, the includes here are truncated to those specific
// for this example.\newline
//
//
// 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 variance maxKernelWidth "
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be chosen for the pixels of the input and output images.
// Image types can be instantiated using the pixel type and dimension.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputPixelType = float;
using OutputPixelType = float;
using InputImageType = itk::Image<InputPixelType, 2>;
using OutputImageType = itk::Image<OutputPixelType, 2>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The discrete Gaussian filter type is instantiated using the
// input and output image types. A corresponding filter object is created.
//
// \index{itk::DiscreteGaussianImageFilter!instantiation}
// \index{itk::DiscreteGaussianImageFilter!New()}
// \index{itk::DiscreteGaussianImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
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 its input.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
const double gaussianVariance = std::stod(argv[3]);
const unsigned int maxKernelWidth = std::stoi(argv[4]);
// Software Guide : BeginLatex
//
// The filter requires the user to provide a value for the variance
// associated with the Gaussian kernel. The method \code{SetVariance()} is
// used for this purpose. The discrete Gaussian is constructed as a
// convolution kernel. The maximum kernel size can be set by the user.
// Note that the combination of variance and kernel-size values may result
// in a truncated Gaussian kernel.
//
// \index{itk::DiscreteGaussianImageFilter!SetVariance()}
// \index{itk::DiscreteGaussianImageFilter!SetMaximumKernelWidth()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetVariance(gaussianVariance);
filter->SetMaximumKernelWidth(maxKernelWidth);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, the filter is executed by invoking the \code{Update()} method.
//
// \index{itk::DiscreteGaussianImageFilter!Update()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->Update();
// Software Guide : EndCodeSnippet
// 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
// trigger the execution of this one. For example, a writer could
// be used after the filter.
//
// Software Guide : EndLatex
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();
writer->SetFileName(argv[2]);
// Software Guide : BeginCodeSnippet
rescaler->SetInput(filter->GetOutput());
writer->SetInput(rescaler->GetOutput());
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.44\textwidth]{DiscreteGaussianImageFilterOutput}
// \itkcaption[DiscreteGaussianImageFilter output]{Effect of the
// DiscreteGaussianImageFilter on a slice from a MRI proton density image of
// the brain.}
// \label{fig:DiscreteGaussianImageFilterInputOutput}
// \end{figure}
//
// Figure~\ref{fig:DiscreteGaussianImageFilterInputOutput} illustrates the
// effect of this filter on a MRI proton density image of the brain.
//
// Note that large Gaussian variances will produce large convolution
// kernels and correspondingly longer computation times. Unless a high
// degree of accuracy is required, it may be more desirable to use the
// approximating \doxygen{RecursiveGaussianImageFilter} with large
// variances.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itk::DiscreteGaussianImageFilter
Blurs an image by separable convolution with discrete gaussian kernels. This filter performs Gaussian...
Definition: itkDiscreteGaussianImageFilter.h:63
Data source that reads image data from a single file.