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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.
*
*=========================================================================*/
// This example demostrates usage of the itk::BayesianClassifierImageFilter
// The input to this example is an itk::VectorImage that represents pixel
// memberships to 'n' classes.
//
// This image is conveniently generated by the BayesianClassifierInitializer.cxx
// example.
//
// The output of the filter is a label map (an image of unsigned char's) with
// pixel values indicating the classes they correspond to. Pixels with intensity 0
// belong to the 0th class, 1 belong to the 1st class etc.... The classification
// is done by applying a Maximum decision rule to the posterior image.
//
// The filter allows you to specify a prior image as well, (although this is not
// done in this example). The prior image, if specified will be a itk::VectorImage
// with as many components as the number of classes. The posterior image is
// then generated by multiplying the prior image with the membership image. If
// the prior image is not specified, the posterior image is the same as the
// membership image.
//
// The filter optionally accepts a smoothingIterations argument. See the
// itk::BayesianClassifierImageFilter for details on how this affects the
// classification. The philosophy is that the filter allows you to iteratively
// smooth the posteriors prior to applying the decision rule. It is hoped
// that this would yield a better classification. The user will need to plug
// in his own smoothing filter. In this case, we specify a
// GradientAnisotropicDiffusionImageFilter.
//
// Example args:
// Memberships.mhd Labelmap.png 3
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkBayesianClassifierImageFilter.h"
#include "itkImageFileWriter.h"
#include "itkGradientAnisotropicDiffusionImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
int main(int argc, char* argv[] )
{
if( argc < 3 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile [smoothingIterations]" << std::endl;
return EXIT_FAILURE;
}
// input parameters
const char * membershipImageFileName = argv[1];
const char * labelMapImageFileName = argv[2];
// setup reader
const unsigned int Dimension = 2;
typedef float InputPixelType;
typedef itk::VectorImage< InputPixelType, Dimension > InputImageType;
typedef itk::ImageFileReader< InputImageType > ReaderType;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( membershipImageFileName );
typedef unsigned char LabelType;
typedef float PriorType;
typedef float PosteriorType;
typedef itk::BayesianClassifierImageFilter<
InputImageType,LabelType,
PosteriorType,PriorType > ClassifierFilterType;
ClassifierFilterType::Pointer filter = ClassifierFilterType::New();
filter->SetInput( reader->GetOutput() );
if( argv[3] )
{
filter->SetNumberOfSmoothingIterations( atoi( argv[3] ));
typedef ClassifierFilterType::ExtractedComponentImageType ExtractedComponentImageType;
typedef itk::GradientAnisotropicDiffusionImageFilter<
ExtractedComponentImageType, ExtractedComponentImageType > SmoothingFilterType;
SmoothingFilterType::Pointer smoother = SmoothingFilterType::New();
smoother->SetNumberOfIterations( 1 );
smoother->SetTimeStep( 0.125 );
smoother->SetConductanceParameter( 3 );
filter->SetSmoothingFilter( smoother );
}
// SET FILTER'S PRIOR PARAMETERS
// do nothing here to default to uniform priors
// otherwise set the priors to some user provided values
//
// Setup writer.. Rescale the label map to the dynamic range of the
// datatype and write it
//
typedef ClassifierFilterType::OutputImageType ClassifierOutputImageType;
typedef itk::Image< unsigned char, Dimension > OutputImageType;
typedef itk::RescaleIntensityImageFilter<
ClassifierOutputImageType, OutputImageType > RescalerType;
RescalerType::Pointer rescaler = RescalerType::New();
rescaler->SetInput( filter->GetOutput() );
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
typedef itk::ImageFileWriter< OutputImageType > WriterType;
WriterType::Pointer writer = WriterType::New();
writer->SetFileName( labelMapImageFileName );
//
// Write labelmap to file
//
writer->SetInput( rescaler->GetOutput() );
try
{
writer->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Exception caught: " << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
// Testing print
filter->Print( std::cout );
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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