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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 is an example of the itk::BayesianClassifierInitializationImageFilter.
// The example's goal is to serve as an initializer for the
// BayesianClassifier.cxx example also found in this directory.
//
// This example takes an input image (to be classified) and generates membership
// images. The membership images determine the degree to which each pixel
// belongs to a class.
//
// The membership image generated by the filter is an
// itk::VectorImage, (with pixels organized as follows: For a 2D image,
// its essentially a 3D array on file with DataType[y][x][c] where c is the
// number of classes and DataType is the template parameter of the filter
// (defaults to float). For a 3D image, it will be organized as
// Datatype[z][y][x][c])
//
// The example also optionally takes in two more arguments, as a convenience to
// the user. These arguements extract the specified component 'c' from the
// membership image and rescale, so the user can fire up a typical image
// viewer and see the relative pixel memberships to class 'c'.
//
// Example args:
// BrainProtonDensitySlice.png Memberships.mhd 4 2 Class2.png
//
// Here Memberships.mhd will be a 2x2x4 image containing pixel memberships
// Class2.png shows pixel memberships to the third class, (rescaled for display)
//
// Notes:
// The default behaviour of the filter is to generate memberships by centering
// gaussian density functions around K-means of the pixel intensities in the
// image. The filter allows you to specify your own membership functions as well.
//
#include "itkImage.h"
#include "itkBayesianClassifierInitializationImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkImageRegionConstIterator.h"
int main(int argc, char *argv[])
{
const unsigned int Dimension = 2;
if( argc < 4 )
{
std::cerr << "Usage arguments: InputImage MembershipImage numberOfClasses [componentToExtract ExtractedImage]" << std::endl;
std::cerr << " The MembershipImage image written is a VectorImage, ( an image with multiple components ) ";
std::cerr << "Given that most viewers can't see vector images, we will optionally extract a component and ";
std::cerr << "write it out as a scalar image as well." << std::endl;
return EXIT_FAILURE;
}
typedef itk::Image< unsigned char, Dimension > ImageType;
typedef itk::BayesianClassifierInitializationImageFilter< ImageType >
BayesianInitializerType;
BayesianInitializerType::Pointer bayesianInitializer
= BayesianInitializerType::New();
typedef itk::ImageFileReader< ImageType > ReaderType;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( argv[1] );
try
{
reader->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Exception thrown " << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
bayesianInitializer->SetInput( reader->GetOutput() );
bayesianInitializer->SetNumberOfClasses( atoi( argv[3] ) );
// TODO add test where we specify membership functions
typedef itk::ImageFileWriter<
BayesianInitializerType::OutputImageType > WriterType;
WriterType::Pointer writer = WriterType::New();
writer->SetInput( bayesianInitializer->GetOutput() );
writer->SetFileName( argv[2] );
try
{
bayesianInitializer->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Exception thrown " << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
try
{
writer->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Exception thrown " << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
if( argv[4] && argv[5] )
{
typedef BayesianInitializerType::OutputImageType MembershipImageType;
typedef itk::Image< MembershipImageType::InternalPixelType,
Dimension > ExtractedComponentImageType;
ExtractedComponentImageType::Pointer extractedComponentImage =
ExtractedComponentImageType::New();
extractedComponentImage->CopyInformation(
bayesianInitializer->GetOutput() );
extractedComponentImage->SetBufferedRegion( bayesianInitializer->GetOutput()->GetBufferedRegion() );
extractedComponentImage->SetRequestedRegion( bayesianInitializer->GetOutput()->GetRequestedRegion() );
extractedComponentImage->Allocate();
typedef itk::ImageRegionConstIterator< MembershipImageType > ConstIteratorType;
typedef itk::ImageRegionIterator< ExtractedComponentImageType > IteratorType;
ConstIteratorType cit( bayesianInitializer->GetOutput(),
bayesianInitializer->GetOutput()->GetBufferedRegion() );
IteratorType it( extractedComponentImage,
extractedComponentImage->GetLargestPossibleRegion() );
const unsigned int componentToExtract = atoi( argv[4] );
cit.GoToBegin();
it.GoToBegin();
while( !cit.IsAtEnd() )
{
it.Set(cit.Get()[componentToExtract]);
++it;
++cit;
}
// Write out the rescaled extracted component
typedef itk::Image< unsigned char, Dimension > OutputImageType;
typedef itk::RescaleIntensityImageFilter<
ExtractedComponentImageType, OutputImageType > RescalerType;
RescalerType::Pointer rescaler = RescalerType::New();
rescaler->SetInput( extractedComponentImage );
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
typedef itk::ImageFileWriter< OutputImageType
> ExtractedComponentWriterType;
ExtractedComponentWriterType::Pointer
rescaledImageWriter = ExtractedComponentWriterType::New();
rescaledImageWriter->SetInput( rescaler->GetOutput() );
rescaledImageWriter->SetFileName( argv[5] );
rescaledImageWriter->Update();
}
return EXIT_SUCCESS;
}
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