Denoise an image using curvature driven flow. More...

#include <itkCurvatureFlowImageFilter.h>

Inheritance diagram for itk::CurvatureFlowImageFilter< TInputImage, TOutputImage >:

Collaboration diagram for itk::CurvatureFlowImageFilter< TInputImage, TOutputImage >:

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef CurvatureFlowFunction < OutputImageType >	CurvatureFlowFunctionType
typedef Superclass::FiniteDifferenceFunctionType	FiniteDifferenceFunctionType
typedef Superclass::InputImageType	InputImageType
typedef OutputImageType::Pointer	OutputImagePointer
typedef Superclass::OutputImageType	OutputImageType
typedef Superclass::PixelType	PixelType
typedef SmartPointer< Self >	Pointer
typedef CurvatureFlowImageFilter	Self
typedef DenseFiniteDifferenceImageFilter < TInputImage, TOutputImage >	Superclass
typedef Superclass::TimeStepType	TimeStepType
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
virtual const char *	GetNameOfClass () const
virtual TimeStepType	GetTimeStep () const
virtual void	SetTimeStep (TimeStepType _arg)
	typedef (Concept::MultiplyOperator< double, PixelType, PixelType >) DoubleOutputMultiplyOperatorCheck
	typedef (Concept::DivisionOperators< PixelType >) OutputDivisionOperatorsCheck
	typedef (Concept::Convertible< PixelType, double >) OutputConvertibleToDoubleCheck
	typedef (Concept::Convertible< double, PixelType >) DoubleConvertibleToOutputCheck
	typedef (Concept::AdditiveOperators< PixelType, double >) OutputDoubleAdditiveOperatorsCheck
	typedef (Concept::MultiplyOperator< int, PixelType, PixelType >) IntOutputMultiplyOperatorCheck
	typedef (Concept::LessThanComparable< PixelType, double >) OutputLessThanDoubleCheck
Static Public Member Functions
static Pointer	New ()
Static Public Attributes
static const unsigned int	ImageDimension = Superclass::ImageDimension
Protected Member Functions
virtual void	EnlargeOutputRequestedRegion (DataObject *)
virtual void	GenerateInputRequestedRegion ()
virtual void	InitializeIteration ()

	CurvatureFlowImageFilter ()
	~CurvatureFlowImageFilter ()
void	PrintSelf (std::ostream &os, Indent indent) const

virtual bool	Halt ()
Private Member Functions
	CurvatureFlowImageFilter (const Self &)
void	operator= (const Self &)
Private Attributes
TimeStepType	m_TimeStep

Detailed Description

template<class TInputImage, class TOutputImage>
class itk::CurvatureFlowImageFilter< TInputImage, TOutputImage >

Denoise an image using curvature driven flow.

CurvatureFlowImageFilter implements a curvature driven image denoising algorithm. Iso-brightness contours in the grayscale input image are viewed as a level set. The level set is then evolved using a curvature-based speed function:

$I_t = \kappa |\nabla I|$

where $\kappa$ is the curvature.

The advantage of this approach is that sharp boundaries are preserved with smoothing occuring only within a region. However, it should be noted that continuous application of this scheme will result in the eventual removal of all information as each contour shrinks to zero and disappear.

Note that unlike level set segmentation algorithms, the image to be denoised is already the level set and can be set directly as the input using the SetInput() method.

This filter has two parameters: the number of update iterations to be performed and the timestep between each update.

The timestep should be "small enough" to ensure numerical stability. Stability is guarantee when the timestep meets the CFL (Courant-Friedrichs-Levy) condition. Broadly speaking, this condition ensures that each contour does not move more than one grid position at each timestep. In the literature, the timestep is typically user specified and have to manually tuned to the application.

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

This filter may be streamed. To support streaming this filter produces a padded output which takes into account edge effects. The size of the padding is m_NumberOfIterations on each edge. Users of this filter should only make use of the center valid central region.

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 floating point type. This filter works for any dimensional images.

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

See also:: DenseFiniteDifferenceImageFilter; CurvatureFlowFunction; MinMaxCurvatureFlowImageFilter; BinaryMinMaxCurvatureFlowImageFilter

Input/Output Restrictions: TInputImage and TOutputImage must have the same dimension. TOutputImage's pixel type must be a real number type.

Definition at line 90 of file itkCurvatureFlowImageFilter.h.