Binary Min and Max Curvature Flow of Binary Image#

Synopsis#

BinaryMinMaxCurvatureFlow a binary image.

Results#

../../../../_images/BinaryMinMaxCurvatureFlow.png — Output In VTK Window#

Code#

C++#

#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkBinaryMinMaxCurvatureFlowImageFilter.h"
#include "itkSubtractImageFilter.h"

#include "itksys/SystemTools.hxx"

#ifdef ENABLE_QUICKVIEW
#  include "QuickView.h"
#endif

int
main(int argc, char * argv[])
{
  if (argc < 2)
  {
    std::cerr << argv[0] << " InputFileName [NumberOfIterations]" << std ::endl;
    return EXIT_FAILURE;
  }

  std::string inputFileName = argv[1];

  unsigned int numberOfIterations = 2;
  if (argc > 2)
  {
    numberOfIterations = std::stoi(argv[2]);
  }

  constexpr unsigned int Dimension = 2;

  using InputPixelType = float;
  using OutputPixelType = float;
  using InputImageType = itk::Image<InputPixelType, Dimension>;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;

  const auto input = itk::ReadImage<InputImageType>(inputFileName);

  using FilterType = itk::BinaryMinMaxCurvatureFlowImageFilter<InputImageType, OutputImageType>;
  auto filter = FilterType::New();
  filter->SetInput(input);
  filter->SetThreshold(255);
  filter->SetNumberOfIterations(numberOfIterations);

  using SubtractType = itk::SubtractImageFilter<OutputImageType>;
  auto diff = SubtractType::New();
  diff->SetInput1(input);
  diff->SetInput2(filter->GetOutput());

#ifdef ENABLE_QUICKVIEW
  QuickView viewer;
  viewer.AddImage(input.GetPointer(), true, itksys::SystemTools::GetFilenameName(inputFileName));

  std::stringstream desc;
  desc << "BinaryMinMaxCurvature, iterations = " << numberOfIterations;
  viewer.AddImage(filter->GetOutput(), true, desc.str());

  std::stringstream desc2;
  desc2 << "Original - BinaryMinMaxCurvatureFlow";
  viewer.AddImage(diff->GetOutput(), true, desc2.str());

  viewer.Visualize();
#endif

  return EXIT_SUCCESS;
}

Classes demonstrated#

template<typename TInputImage, typename TOutputImage> class BinaryMinMaxCurvatureFlowImageFilter : public itk::MinMaxCurvatureFlowImageFilter<TInputImage, TOutputImage>

Denoise a binary image using min/max curvature flow.

BinaryMinMaxCurvatureFlowImageFilter implements a curvature driven image denoising algorithm. This filter assumes that the image is essentially binary: consisting of two classes. Iso-brightness contours in the input image are viewed as a level set. The level set is then evolved using a curvature-based speed function:

$I_t = F_{\mbox{minmax}} |\nabla I|$

where $F_{\mbox{minmax}} = \min(\kappa,0)$ if $\mbox{Avg}_{\mbox{stencil}}(x)$ is less than or equal to $T_{threshold}$ and $\max(\kappa,0)$ , otherwise. $\kappa$ is the mean curvature of the iso-brightness contour at point $x$ .

In min/max curvature flow, movement is turned on or off depending on the scale of the noise one wants to remove. Switching depends on the average image value of a region of radius $R$ around each point. The choice of $R$ , the stencil radius, governs the scale of the noise to be removed.

The threshold value $T_{threshold}$ is a user specified value which discriminates between the two pixel classes.

This filter make use of the multi-threaded finite difference solver hierarchy. Updates are computed using a BinaryMinMaxCurvatureFlowFunction object. A zero flux Neumann boundary condition is used when computing derivatives near the data boundary.

Reference: “Level Set Methods and Fast Marching Methods”, J.A. Sethian, Cambridge Press, Chapter 16, Second edition, 1999.

Warning: This filter assumes that the input and output types have the same dimensions. This filter also requires that the output image pixels are of a real type. This filter works for any dimensional images.

See

BinaryMinMaxCurvatureFlowFunction

See

CurvatureFlowImageFilter

See

MinMaxCurvatureFlowImageFilter

ITK Sphinx Examples: