ITK  4.8.0 Insight Segmentation and Registration 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
typedef float InputPixelType;
typedef float OutputPixelType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// With them, the input and output image types can be instantiated.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::Image< InputPixelType, 2 > InputImageType;
typedef itk::Image< OutputPixelType, 2 > OutputImageType;
// 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
InputImageType, OutputImageType > FilterType;
// 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 = atof( 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
typedef unsigned char WritePixelType;
typedef itk::Image< WritePixelType, 2 > WriteImageType;
OutputImageType, WriteImageType > 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
// the GradientMagnitudeRecursiveGaussianImageFilter on a slice from a MRI
// proton density image of the brain.}
// \end{figure}
//
// Figure
// the brain using $\sigma$ values of $3$ (left) and $5$
// regulated by selecting an appropriate $\sigma$. This type of