ITK  4.6.0
Insight Segmentation and Registration Toolkit
Filtering/LaplacianRecursiveGaussianImageFilter2.cxx
/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// ARGUMENTS: LaplacianRecursiveGaussianImageFilter2Output3.mha 3
// OUTPUTS: {LaplacianRecursiveGaussianImageFilter2Output3.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// ARGUMENTS: LaplacianRecursiveGaussianImageFilter2Output5.mha 5
// OUTPUTS: {LaplacianRecursiveGaussianImageFilter2Output5.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The previous exampled showed how to use the
// \doxygen{RecursiveGaussianImageFilter} for computing the equivalent of a
// Laplacian of an image after smoothing with a Gaussian. The elements used
// in this previous example have been packaged together in the
// \doxygen{LaplacianRecursiveGaussianImageFilter} in order to simplify its
// usage. This current example shows how to use this convenience filter for
// achieving the same results as the previous example.
//
// \index{itk::LaplacianRecursiveGaussianImageFilter}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
//
// \index{itk::LaplacianRecursiveGaussianImageFilter!header}
//
// 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 [RescaledOutputImageFile] " << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be selected on the desired input and output pixel types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef float InputPixelType;
typedef float OutputPixelType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input and output image types are instantiated using the pixel types.
//
// 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.
//
// \index{itk::RecursiveGaussianImageFilter!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InputImageType, OutputImageType > FilterType;
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( argv[1] );
// Software Guide : BeginLatex
//
// This filter packages all the components illustrated in the previous
// example. The filter is created by invoking the \code{New()} method and
// assigning the result to a \doxygen{SmartPointer}.
//
// \index{itk::LaplacianRecursiveGaussianImageFilter!New()}
// \index{itk::LaplacianRecursiveGaussianImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FilterType::Pointer laplacian = FilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The option for normalizing across scale space can also be selected in this filter.
//
// \index{LaplacianRecursiveGaussianImageFilter!SetNormalizeAcrossScale()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
laplacian->SetNormalizeAcrossScale( false );
// 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 the source.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
laplacian->SetInput( reader->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// It is now time to select the $\sigma$ of the Gaussian used to smooth the
// data. Note that $\sigma$ must be passed to both filters and that sigma
// is considered to be in millimeters. That is, at the moment of applying
// the smoothing process, the filter will take into account the spacing
// values defined in the image.
//
// \index{itk::LaplacianRecursiveGaussianImageFilter!SetSigma()}
// \index{SetSigma()!itk::LaplacianRecursiveGaussianImageFilter}
//
// Software Guide : EndLatex
const double sigma = atof( argv[3] );
// Software Guide : BeginCodeSnippet
laplacian->SetSigma( sigma );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally the pipeline is executed by invoking the \code{Update()} method.
//
// \index{itk::LaplacianRecursiveGaussianImageFilter!Update()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
laplacian->Update();
}
catch( itk::ExceptionObject & err )
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// The image can also be saved into a file, by using the ImageFileWriter.
//
typedef float WritePixelType;
typedef itk::Image< WritePixelType, 2 > WriteImageType;
WriterType::Pointer writer = WriterType::New();
writer->SetInput( laplacian->GetOutput() );
writer->SetFileName( argv[2] );
writer->Update();
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{LaplacianRecursiveGaussianImageFilter2Output3}
// \includegraphics[width=0.44\textwidth]{LaplacianRecursiveGaussianImageFilter2Output5}
// \itkcaption[Output of the LaplacianRecursiveGaussianImageFilter.]{Effect of the
// LaplacianRecursiveGaussianImageFilter on a slice from a MRI proton density image
// of the brain.}
// \label{fig:RecursiveGaussianImageFilter2InputOutput}
// \end{figure}
//
// Figure~\ref{fig:RecursiveGaussianImageFilter2InputOutput} 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
// attenuation of noise can be regulated by selecting the appropriate
// standard deviation. This type of scale-tunable filter is suitable for
// performing scale-space analysis.
//
// Software Guide : EndLatex
// Rescale float outputs to png for inclusion in the Software guide
//
if (argc > 4)
{
typedef unsigned char CharPixelType;
typedef itk::Image<CharPixelType, 2> CharImageType;
RescaleFilterType;
RescaleFilterType::Pointer rescale = RescaleFilterType::New();
rescale->SetInput( laplacian->GetOutput() );
rescale->SetOutputMinimum( 0 );
rescale->SetOutputMaximum( 255 );
typedef itk::ImageFileWriter< CharImageType > CharWriterType;
CharWriterType::Pointer charWriter = CharWriterType::New();
charWriter->SetFileName( argv[4] );
charWriter->SetInput( rescale->GetOutput() );
charWriter->Update();
}
return EXIT_SUCCESS;
}