#include <itkBinaryMinMaxCurvatureFlowImageFilter.h>

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

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

Public Types
typedef BinaryMinMaxCurvatureFlowFunction < OutputImageType >	BinaryMinMaxCurvatureFlowFunctionType
typedef SmartPointer< const Self >	ConstPointer
typedef Superclass::FiniteDifferenceFunctionType	FiniteDifferenceFunctionType
typedef Superclass::OutputImageType	OutputImageType
typedef SmartPointer< Self >	Pointer
typedef BinaryMinMaxCurvatureFlowImageFilter	Self
typedef MinMaxCurvatureFlowImageFilter < TInputImage, TOutputImage >	Superclass
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
virtual const char *	GetNameOfClass () const
	typedef (Concept::Convertible< typename TInputImage::PixelType, typename TOutputImage::PixelType >) InputConvertibleToOutputCheck

virtual void	SetThreshold (double _arg)
virtual double	GetThreshold () const
Static Public Member Functions
static Pointer	New ()
Static Public Attributes
static const unsigned int	ImageDimension = Superclass::ImageDimension
Protected Member Functions
virtual void	InitializeIteration ()

	BinaryMinMaxCurvatureFlowImageFilter ()
	~BinaryMinMaxCurvatureFlowImageFilter ()
void	PrintSelf (std::ostream &os, Indent indent) const
Private Member Functions
	BinaryMinMaxCurvatureFlowImageFilter (const Self &)
void	operator= (const Self &)
Private Attributes
double	m_Threshold

Detailed Description

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

Denoise a binary image using min/max curvature flow.

BinaryMinMaxCurvatureFlowImageFilter implements a curvature driven image denosing 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 $T_{thresold}$ 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.

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.

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

See also:: BinaryMinMaxCurvatureFlowFunction; CurvatureFlowImageFilter; MinMaxCurvatureFlowImageFilter

Definition at line 76 of file itkBinaryMinMaxCurvatureFlowImageFilter.h.