itkshapedetectionlevelsetimagefiltertest.cxx

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/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: itkShapeDetectionLevelSetImageFilterTest.cxx,v $
  Language:  C++
  Date:      $Date: 2003-10-07 16:43:01 $
  Version:   $Revision: 1.12 $

  Copyright (c) Insight Software Consortium. All rights reserved.
  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

     This software is distributed WITHOUT ANY WARRANTY; without even 
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#include "itkShapeDetectionLevelSetImageFilter.h"

#include "itkImage.h"
#include "itkImageRegionIterator.h"
#include "itkCastImageFilter.h"
#include "itkGradientMagnitudeRecursiveGaussianImageFilter.h"
#include "itkSigmoidImageFilter.h"
#include "itkFastMarchingImageFilter.h"
#include "itkBinaryThresholdImageFilter.h"
#include "itkSimilarityIndexImageFilter.h"

/* Uncomment to write out image files */
/*
#include "itkRescaleIntensityImageFilter.h"
#include "itkImageFileWriter.h"
*/

int itkShapeDetectionLevelSetImageFilterTest(int, char* [] )
{

  const   unsigned int    ImageDimension = 2;
  typedef unsigned char   PixelType;
  typedef float           InternalPixelType;

  typedef itk::Image<PixelType,ImageDimension> ImageType;
  typedef itk::Image<InternalPixelType,ImageDimension> InternalImageType;

  ImageType::SizeType imageSize;
  imageSize[0] = 128;
  imageSize[1] = 128;

  ImageType::RegionType imageRegion;
  imageRegion.SetSize( imageSize );

  /**
   * Create an input image.
   * A light square on a dark background.
   */
  PixelType background = 0;
  PixelType foreground = 190;

  ImageType::Pointer inputImage = ImageType::New();
  inputImage->SetRegions( imageRegion );
  inputImage->Allocate();
  inputImage->FillBuffer( background );

  ImageType::IndexType squareStart;
  squareStart.Fill( 20 );
  ImageType::SizeType squareSize;
  squareSize.Fill( 60 );
  ImageType::RegionType squareRegion;
  squareRegion.SetIndex( squareStart );
  squareRegion.SetSize( squareSize );

  typedef itk::ImageRegionIterator<ImageType> Iterator;
  Iterator it( inputImage, squareRegion );
  it.GoToBegin();
  while( !it.IsAtEnd() )
    {
    it.Set( foreground );
    ++it;
    }

  try
    {
    /**
    * Create an edge potential map.
    * First compute the image gradient magnitude using a derivative of gaussian filter.
    * Then apply a sigmoid function to the gradient magnitude.
    */
    typedef itk::CastImageFilter< ImageType, InternalImageType > CastFilterType;
    CastFilterType::Pointer caster = CastFilterType::New();
    caster->SetInput( inputImage );

    typedef itk::GradientMagnitudeRecursiveGaussianImageFilter< InternalImageType,
      InternalImageType > GradientImageType;

    GradientImageType::Pointer gradMagnitude = GradientImageType::New();
    gradMagnitude->SetInput( caster->GetOutput() );
    gradMagnitude->SetSigma( 1.0 );

    typedef itk::SigmoidImageFilter< InternalImageType, InternalImageType >
      SigmoidFilterType;
    SigmoidFilterType::Pointer sigmoid = SigmoidFilterType::New();
    sigmoid->SetOutputMinimum( 0.0 );
    sigmoid->SetOutputMaximum( 1.0 );
    sigmoid->SetAlpha( -0.4 );
    sigmoid->SetBeta( 2.5 );
    sigmoid->SetInput( gradMagnitude->GetOutput() );

    /**
    * Create an initial level.
    * Use fast marching to create an signed distance from a seed point.
    */
    typedef itk::FastMarchingImageFilter<InternalImageType> FastMarchingFilterType;
    FastMarchingFilterType::Pointer fastMarching = FastMarchingFilterType::New();

    typedef FastMarchingFilterType::NodeContainer NodeContainer;
    typedef FastMarchingFilterType::NodeType      NodeType;

    NodeContainer::Pointer seeds = NodeContainer::New();

    // Choose an initial contour that is wholly within the square to be segmented.
    InternalImageType::IndexType seedPosition;
    seedPosition[0] = 47;
    seedPosition[1] = 47;

    NodeType node;
    node.SetValue( -5.0 );
    node.SetIndex( seedPosition );

    seeds->Initialize();
    seeds->InsertElement( 0, node );

    fastMarching->SetTrialPoints( seeds );
    fastMarching->SetSpeedConstant( 1.0 );
    fastMarching->SetOutputSize( imageSize );

    /**
    * Set up and run the shape detection filter
    */
    typedef itk::ShapeDetectionLevelSetImageFilter<
      InternalImageType, InternalImageType > ShapeDetectionFilterType;
    
    ShapeDetectionFilterType::Pointer shapeDetection = ShapeDetectionFilterType::New();
    
    // set the initial level set
    shapeDetection->SetInput( fastMarching->GetOutput() );

    // set the edge potential image
    shapeDetection->SetFeatureImage( sigmoid->GetOutput() );

    // set the weights between the propagation and curvature terms
    shapeDetection->SetPropagationScaling( 1.0 );
    shapeDetection->SetCurvatureScaling( 0.1 );

    // set the convergence criteria
    shapeDetection->SetMaximumRMSError( 0.02 );
    shapeDetection->SetNumberOfIterations( 200 );

    /**
    * Threshold the output level set to display the final contour.
    */
    typedef itk::BinaryThresholdImageFilter< InternalImageType, ImageType >
      ThresholdFilterType;
    ThresholdFilterType::Pointer thresholder = ThresholdFilterType::New();

    thresholder->SetInput( shapeDetection->GetOutput() );
    thresholder->SetLowerThreshold( -1e+10 );
    thresholder->SetUpperThreshold( 0.0 );
    thresholder->SetOutsideValue( 0 );
    thresholder->SetInsideValue( 255 );

    /**
    * Compute overlap between the true shape and the segmented shape
    */
    typedef itk::SimilarityIndexImageFilter< ImageType, ImageType >
      OverlapCalculatorType;
    OverlapCalculatorType::Pointer overlap = OverlapCalculatorType::New();

    overlap->SetInput1( inputImage );
    overlap->SetInput2( thresholder->GetOutput() );
    overlap->Update();
    
    /** Printout useful information from the shape detection filter. */
    std::cout << "Max. no. iterations: " << shapeDetection->GetNumberOfIterations() << std::endl;
    std::cout << "Max. RMS error: " << shapeDetection->GetMaximumRMSError() << std::endl;
    std::cout << "No. elpased iterations: " << shapeDetection->GetElapsedIterations() << std::endl;
    std::cout << "RMS change: " << shapeDetection->GetRMSChange() << std::endl;
    std::cout << "Overlap: " << overlap->GetSimilarityIndex() << std::endl;


    /**
    * Uncomment to write out image files.
    */
  /* 
    typedef itk::ImageFileWriter< ImageType > WriterType;
    WriterType::Pointer writer = WriterType::New();

    typedef itk::RescaleIntensityImageFilter< InternalImageType,
      ImageType > RescaleFilterType;
    RescaleFilterType::Pointer rescaler = RescaleFilterType::New();

    writer->SetFileName( "inputImage.png" );
    writer->SetInput( inputImage );
    writer->Update();

    rescaler->SetInput( gradMagnitude->GetOutput() );
    rescaler->SetOutputMinimum( 0 );
    rescaler->SetOutputMaximum( 255 );
    writer->SetFileName( "gradMagnitude.png" );
    writer->SetInput( rescaler->GetOutput() );
    writer->Update();

    rescaler->SetInput( sigmoid->GetOutput() );
    writer->SetFileName( "edgePotential.png" );
    writer->Update();

    writer->SetInput( thresholder->GetOutput() );
    writer->SetFileName( "outputLevelSet.png" );
    writer->Update();

    thresholder->SetInput( fastMarching->GetOutput() );
    writer->SetInput( thresholder->GetOutput() );
    writer->SetFileName( "initialLevelSet.png" );
    writer->Update();
*/
    // Check of overlap is above threshold
    if ( overlap->GetSimilarityIndex() > 0.90 )
      {
      std::cout << "Overlap exceed threshold." << std::endl;
      }
    else
      {
      std::cout << "Overlap below threshold." << std::endl;
      std::cout << "Test failed." << std::endl;
      return EXIT_FAILURE;
      }

    // Test case when PropagationScaling is zero
    shapeDetection->SetPropagationScaling( 0.0 );
    shapeDetection->SetCurvatureScaling( 1.0 );
    shapeDetection->Update();

    std::cout << "Test Passed. " << std::endl;
    return EXIT_SUCCESS;

    }
  catch( itk::ExceptionObject& err )
    {
    std::cout << err << std::endl;
    return EXIT_FAILURE;
    }
  catch( ... )
    {
    std::cout << "Caught unknown exception" << std::endl;
    return EXIT_FAILURE;
    }



}