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/*=========================================================================
*
* 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}
// OUTPUTS: {MeanImageFilterOutput.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The \doxygen{MeanImageFilter} is commonly used for noise reduction. The
// filter computes the value of each output pixel by finding the statistical
// mean of the neighborhood of the corresponding input pixel. The following
// figure illustrates the local effect of the MeanImageFilter in a $2D$
// case. The statistical mean of the neighborhood on the left is passed as the
// output value associated with the pixel at the center of the neighborhood.
//
// \begin{center}
// \begin{picture}(200,46)
// \put( 5.0, 0.0 ){\framebox(30.0,15.0){25}}
// \put( 35.0, 0.0 ){\framebox(30.0,15.0){30}}
// \put( 65.0, 0.0 ){\framebox(30.0,15.0){32}}
// \put( 5.0, 15.0 ){\framebox(30.0,15.0){27}}
// \put( 35.0, 15.0 ){\framebox(30.0,15.0){25}}
// \put( 65.0, 15.0 ){\framebox(30.0,15.0){29}}
// \put( 5.0, 30.0 ){\framebox(30.0,15.0){28}}
// \put( 35.0, 30.0 ){\framebox(30.0,15.0){26}}
// \put( 65.0, 30.0 ){\framebox(30.0,15.0){50}}
// \put( 100.0, 22.0 ){\vector(1,0){20.0}}
// \put( 125.0, 15.0 ){\framebox(34.0,15.0){30.22}}
// \put( 160.0, 22.0 ){\vector(1,0){20.0}}
// \put( 185.0, 15.0 ){\framebox(30.0,15.0){30}}
// \end{picture}
// \end{center}
//
// Note that this algorithm is sensitive to the presence of outliers in the
// neighborhood. This filter will work on images of any dimension thanks to
// the internal use of \doxygen{SmartNeighborhoodIterator} and
// \doxygen{NeighborhoodOperator}. The size of the neighborhood over which
// the mean is computed can be set by the user.
//
// \index{itk::MeanImageFilter}
//
// Software Guide : EndLatex
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
// Software Guide : BeginLatex
//
// The header file corresponding to this filter should be included first.
//
// \index{itk::MeanImageFilter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkMeanImageFilter.h"
// Software Guide : EndCodeSnippet
int main( int argc, char * argv[] )
{
if( argc < 3 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Then the pixel types for input and output image must be defined and, with
// them, the image types can be instantiated.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef unsigned char InputPixelType;
typedef unsigned char OutputPixelType;
typedef itk::Image< InputPixelType, 2 > InputImageType;
typedef itk::Image< OutputPixelType, 2 > OutputImageType;
// Software Guide : EndCodeSnippet
typedef itk::ImageFileReader< InputImageType > ReaderType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName( argv[1] );
writer->SetFileName( argv[2] );
// Software Guide : BeginLatex
//
// Using the image types it is now possible to instantiate the filter type
// and create the filter object.
//
// \index{itk::MeanImageFilter!instantiation}
// \index{itk::MeanImageFilter!New()}
// \index{itk::MeanImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::MeanImageFilter<
InputImageType, OutputImageType > FilterType;
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The size of the neighborhood is defined along every dimension by
// passing a \code{SizeType} object with the corresponding values. The
// value on each dimension is used as the semi-size of a rectangular
// box. For example, in $2D$ a size of \(1,2\) will result in a $3 \times
// 5$ neighborhood.
//
// \index{itk::MeanImageFilter!Radius}
// \index{itk::MeanImageFilter!Neighborhood}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InputImageType::SizeType indexRadius;
indexRadius[0] = 1; // radius along x
indexRadius[1] = 1; // radius along y
filter->SetRadius( indexRadius );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input to the filter can be taken from any other filter, for example
// a reader. The output can be passed down the pipeline to other filters,
// for example, a writer. An update call on any downstream filter will
// trigger the execution of the mean filter.
//
// \index{itk::MeanImageFilter!SetInput()}
// \index{itk::MeanImageFilter!GetOutput()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetInput( reader->GetOutput() );
writer->SetInput( filter->GetOutput() );
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.44\textwidth]{MeanImageFilterOutput}
// \itkcaption[Effect of the MedianImageFilter]{Effect of the MeanImageFilter on a slice
// from a MRI proton density brain image.}
// \label{fig:MeanImageFilterOutput}
// \end{figure}
//
// Figure \ref{fig:MeanImageFilterOutput} illustrates the effect of this
// filter on a slice of MRI brain image using neighborhood radii of
// \(1,1\) which corresponds to a $ 3 \times 3 $ classical neighborhood.
// It can be seen from this picture that edges are rapidly degraded by the
// diffusion of intensity values among neighbors.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
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