ITK  4.9.0
Insight Segmentation and Registration Toolkit
Examples/Segmentation/ConfidenceConnected3D.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: {brainweb165a10f17.mha}
// ARGUMENTS: {WhiteMatterSegmentation.mhd}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example is a 3D version of the previous ConfidenceConnected example.
// In this particular case, we are extracting the white matter from an input
// Brain MRI dataset.
//
// Software Guide : EndLatex
int main( int argc, char *argv[] )
{
if( argc < 3 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage outputImage " << std::endl;
return EXIT_FAILURE;
}
typedef float InternalPixelType;
const unsigned int Dimension = 3;
typedef itk::Image< InternalPixelType, Dimension > InternalImageType;
typedef unsigned char OutputPixelType;
CastingFilterType;
CastingFilterType::Pointer caster = CastingFilterType::New();
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName( argv[1] );
writer->SetFileName( argv[2] );
CurvatureFlowImageFilterType;
CurvatureFlowImageFilterType::Pointer smoothing =
CurvatureFlowImageFilterType::New();
ConnectedFilterType;
ConnectedFilterType::Pointer confidenceConnected = ConnectedFilterType::New();
smoothing->SetInput( reader->GetOutput() );
confidenceConnected->SetInput( smoothing->GetOutput() );
caster->SetInput( confidenceConnected->GetOutput() );
writer->SetInput( caster->GetOutput() );
smoothing->SetNumberOfIterations( 2 );
smoothing->SetTimeStep( 0.05 );
confidenceConnected->SetMultiplier( 2.5 );
confidenceConnected->SetNumberOfIterations( 5 );
confidenceConnected->SetInitialNeighborhoodRadius( 2 );
confidenceConnected->SetReplaceValue( 255 );
InternalImageType::IndexType index1;
index1[0] = 118;
index1[1] = 133;
index1[2] = 92;
confidenceConnected->AddSeed( index1 );
InternalImageType::IndexType index2;
index2[0] = 63;
index2[1] = 135;
index2[2] = 94;
confidenceConnected->AddSeed( index2 );
InternalImageType::IndexType index3;
index3[0] = 63;
index3[1] = 157;
index3[2] = 90;
confidenceConnected->AddSeed( index3 );
InternalImageType::IndexType index4;
index4[0] = 111;
index4[1] = 150;
index4[2] = 90;
confidenceConnected->AddSeed( index4 );
InternalImageType::IndexType index5;
index5[0] = 111;
index5[1] = 50;
index5[2] = 88;
confidenceConnected->AddSeed( index5 );
try
{
writer->Update();
}
catch( itk::ExceptionObject & excep )
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}