ITK  5.3.0 Insight Toolkit
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// ARGUMENTS: 3
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// ARGUMENTS: 5
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// Differentiation is an ill-defined operation over digital data. In practice
// it is convenient to define a scale in which the differentiation should be
// performed. This is usually done by preprocessing the data with a smoothing
// filter. It has been shown that a Gaussian kernel is the most convenient
// choice for performing such smoothing. By choosing a particular value for
// the standard deviation ($\sigma$) of the Gaussian, an associated scale is
// selected that ignores high frequency content, commonly considered image
// noise.
//
// magnitude of the image gradient at each pixel location. The computational
// process is equivalent to first smoothing the image by convolving it with a
// Gaussian kernel and then applying a differential operator. The user
// selects the value of $\sigma$.
//
// Internally this is done by applying an IIR \footnote{Infinite Impulse
// Response} filter that approximates a convolution with the derivative of
// the Gaussian kernel. Traditional convolution will produce a more accurate
// result, but the IIR approach is much faster, especially using large
// $\sigma$s \cite{Deriche1990,Deriche1993}.
//
// GradientMagnitudeRecursiveGaussianImageFilter will work on images of
// any dimension by taking advantage of the natural separability of the
// Gaussian kernel and its derivatives.
//
//
// 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 < 4)
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile sigma"
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be instantiated based on the pixels of the input and
// output images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputPixelType = float;
using OutputPixelType = float;
// 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, 2>;
using OutputImageType = itk::Image<OutputPixelType, 2>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filter type is now instantiated using both the input image and the
// output image types.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
OutputImageType>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// A filter object is created by invoking the \code{New()} method and
// assigning the result to a \doxygen{SmartPointer}.
//
//
// 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
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The standard deviation of the Gaussian smoothing kernel is now set.
//
//
// Software Guide : EndLatex
const double sigma = std::stod(argv[3]);
// Software Guide : BeginCodeSnippet
filter->SetSigma(sigma);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally the filter is executed by invoking the \code{Update()} method.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// If connected to other filters in a pipeline, this filter will
// automatically update when any downstream filters are updated. For
// example, we may connect this gradient magnitude filter to an image file
// writer and then update the writer.
//
// 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
// of the GradientMagnitudeRecursiveGaussianImageFilter on a slice from a
// MRI proton density image of the brain.}
// \end{figure}
//
// Figure
// illustrates the effect of this filter on a MRI proton density image of
// the brain using $\sigma$ values of $3$ (left) and $5$
// (right). The figure shows how the sensitivity to noise can be
// regulated by selecting an appropriate $\sigma$. This type of
// scale-tunable filter is suitable for performing scale-space analysis.
//
// Attention should be paid to the image type chosen to represent the output
// image since the dynamic range of the gradient magnitude image is usually
// smaller than the dynamic range of the input image.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
Data source that reads image data from a single file.
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:88