ITK  4.6.0
Insight Segmentation and Registration Toolkit
Filtering/DanielssonDistanceMapImageFilter.cxx
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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
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* http://www.apache.org/licenses/LICENSE-2.0.txt
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* Unless required by applicable law or agreed to in writing, software
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
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// Software Guide : BeginCommandLineArgs
// INPUTS: {FivePoints.png}
// OUTPUTS: {DanielssonDistanceMapImageFilterOutput1.png}
// OUTPUTS: {DanielssonDistanceMapImageFilterOutput2.png}
// ARGUMENTS: {DanielssonDistanceMapImageFilterOutput3.mhd}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the
// \doxygen{DanielssonDistanceMapImageFilter}. This filter generates a
// distance map from the input image using the algorithm developed by
// Danielsson \cite{Danielsson1980}. As secondary outputs, a Voronoi
// partition of the input elements is produced, as well as a vector image
// with the components of the distance vector to the closest point. The input
// to the map is assumed to be a set of points on the input image. Each
// point/pixel is considered to be a separate entity even if they share the
// same gray level value.
//
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!Instantiation}
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!Header}
//
// The first step required to use this filter is to include its header file.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
#include "itkImage.h"
int main( int argc, char * argv[] )
{
if( argc < 5 )
{
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImageFile outputDistanceMapImageFile ";
std::cerr << " outputVoronoiMapImageFile ";
std::cerr << " outputVectorMapImageFile ";
std::cerr << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Then we must decide what pixel types to use for the input and output
// images. Since the output will contain distances measured in pixels, the
// pixel type should be able to represent at least the width of the image,
// or said in $N-D$ terms, the maximum extension along all the dimensions.
// The input and output image types are now defined using their respective
// pixel type and dimension.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef unsigned char InputPixelType;
typedef unsigned short OutputPixelType;
typedef itk::Image< InputPixelType, 2 > InputImageType;
typedef itk::Image< OutputPixelType, 2 > OutputImageType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filter type can be instantiated using the input and output image
// types defined above. A filter object is created with the \code{New()}
// method.
//
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!instantiation}
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!New()}
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InputImageType, OutputImageType, OutputImageType > FilterType;
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
OutputImageType, OutputImageType > RescalerType;
RescalerType::Pointer scaler = RescalerType::New();
//
// Reader and Writer types are instantiated.
//
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName( argv[1] );
writer->SetFileName( argv[2] );
// Software Guide : BeginLatex
//
// The input to the filter is taken from a reader and its output is passed
// to a \doxygen{RescaleIntensityImageFilter} and then to a writer.
//
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!SetInput()}
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!GetOutput()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetInput( reader->GetOutput() );
scaler->SetInput( filter->GetOutput() );
writer->SetInput( scaler->GetOutput() );
// Software Guide : EndCodeSnippet
scaler->SetOutputMaximum( 65535L );
scaler->SetOutputMinimum( 0L );
// Software Guide : BeginLatex
//
// The type of input image has to be specified. In this case, a binary
// image is selected.
//
// \index{itk::Danielsson\-Distance\-MapImage\-Filter!InputIsBinaryOn()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->InputIsBinaryOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{FivePoints}
// \includegraphics[width=0.32\textwidth]{DanielssonDistanceMapImageFilterOutput1}
// \includegraphics[width=0.32\textwidth]{DanielssonDistanceMapImageFilterOutput2}
// \itkcaption[DanielssonDistanceMapImageFilter
// output]{DanielssonDistanceMapImageFilter output. Set of pixels, distance
// map and Voronoi partition.}
// \label{fig:DanielssonDistanceMapImageFilterInputOutput}
// \end{figure}
//
// Figure \ref{fig:DanielssonDistanceMapImageFilterInputOutput} illustrates
// the effect of this filter on a binary image with a set of points. The
// input image is shown at left, the distance map at the center and the
// Voronoi partition at right. This filter computes distance maps in
// N-dimensions and is therefore capable of producing $N-D$ Voronoi
// partitions.
//
// \index{Voronoi partitions}
// \index{Voronoi partitions!itk::Danielsson\-Distance\-Map\-Image\-Filter}
//
// Software Guide : EndLatex
writer->Update();
const char * voronoiMapFileName = argv[3];
// Software Guide : BeginLatex
//
// The Voronoi map is obtained with the \code{GetVoronoiMap()} method. In
// the lines below we connect this output to the intensity rescaler and
// save the result in a file.
//
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!GetVoronoiMap()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
scaler->SetInput( filter->GetVoronoiMap() );
writer->SetFileName( voronoiMapFileName );
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The distance filter also produces an image of \doxygen{Offset} pixels
// representing the vectorial distance to the closest object in the scene.
// The type of this output image is defined by the VectorImageType
// trait of the filter type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef FilterType::VectorImageType OffsetImageType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We can use this type for instantiating an \doxygen{ImageFileWriter} type
// and creating an object of this class in the following lines.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::ImageFileWriter< OffsetImageType > WriterOffsetType;
WriterOffsetType::Pointer offsetWriter = WriterOffsetType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output of the distance filter can be connected as input to the
// writer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
offsetWriter->SetInput( filter->GetVectorDistanceMap() );
// Software Guide : EndCodeSnippet
offsetWriter->SetFileName( argv[4] );
// Software Guide : BeginLatex
//
// Execution of the writer is triggered by the invocation of the
// \code{Update()} method. Since this method can potentially throw
// exceptions it must be placed in a \code{try/catch} block.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
offsetWriter->Update();
}
catch( itk::ExceptionObject & exp )
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << exp << std::endl;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Note that only the \doxygen{MetaImageIO} class supports reading and
// writing images of pixel type \doxygen{Offset}.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}