ITK  4.13.0 Insight Segmentation and Registration Toolkit
Examples/Filtering/DiscreteGaussianImageFilter.cxx
/*=========================================================================
*
*
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*
* Unless required by applicable law or agreed to in writing, software
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
*
*=========================================================================*/
// 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
typedef float InputPixelType;
typedef float OutputPixelType;
typedef itk::Image< InputPixelType, 2 > InputImageType;
typedef itk::Image< OutputPixelType, 2 > OutputImageType;
// 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
InputImageType, OutputImageType > 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 = atof( argv[3] );
const unsigned int maxKernelWidth = atoi( 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
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
// \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;
}