Segments structures in images based on user supplied edge potential map. More...

#include <itkCurvesLevelSetImageFilter.h>

Inheritance diagram for itk::CurvesLevelSetImageFilter< TInputImage, TFeatureImage, TOutputPixelType >:

Collaboration diagram for itk::CurvesLevelSetImageFilter< TInputImage, TFeatureImage, TOutputPixelType >:

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef CurvesFunctionType::Pointer	CurvesFunctionPointer
typedef CurvesLevelSetFunction < OutputImageType, FeatureImageType >	CurvesFunctionType
typedef Superclass::FeatureImageType	FeatureImageType
typedef Superclass::OutputImageType	OutputImageType
typedef SmartPointer< Self >	Pointer
typedef CurvesLevelSetImageFilter	Self
typedef SegmentationLevelSetImageFilter < TInputImage, TFeatureImage, TOutputPixelType >	Superclass
typedef Superclass::ValueType	ValueType
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
float	GetDerivativeSigma () const
virtual const char *	GetNameOfClass () const
	typedef (Concept::HasNumericTraits< TOutputPixelType >) OutputHasNumericTraitsCheck

void	SetDerivativeSigma (float value)
Static Public Member Functions
static Pointer	New ()
Protected Member Functions
	CurvesLevelSetImageFilter ()
	CurvesLevelSetImageFilter (const Self &)
void	GenerateData ()
void	operator= (const Self &)
virtual void	PrintSelf (std::ostream &os, Indent indent) const
	~CurvesLevelSetImageFilter ()
Private Attributes
CurvesFunctionPointer	m_CurvesFunction

Detailed Description

template<class TInputImage, class TFeatureImage, class TOutputPixelType = float>
class itk::CurvesLevelSetImageFilter< TInputImage, TFeatureImage, TOutputPixelType >

Segments structures in images based on user supplied edge potential map.

IMPORTANT: The SegmentationLevelSetImageFilter class and the CurvesLevelSetFunction class contain additional information necessary to the full understanding of how to use this filter.

OVERVIEW: This class is a level set method segmentation filter. An initial contour is propagated outwards (or inwards) until it sticks to the shape boundaries. This is done by using a level set speed function based on a user supplied edge potential map.

INPUTS: This filter requires two inputs. The first input is a initial level set. The initial level set is a real image which contains the initial contour/surface as the zero level set. For example, a signed distance function from the initial contour/surface is typically used. Unlike the simpler ShapeDetectionLevelSetImageFilter the initial contour does not have to lie wholly within the shape to be segmented. The intiial contour is allow to overlap the shape boundary. The extra advection term in the update equation behaves like a doublet and attracts the contour to the boundary. This approach for segmentation follows that of Lorigo et al (2001).

: The second input is the feature image. For this filter, this is the edge potential map. General characteristics of an edge potential map is that it has values close to zero in regions near the edges and values close to one inside the shape itself. Typically, the edge potential map is compute from the image gradient, for example:

$g(I) = 1 / ( 1 + | (\nabla * G)(I)| )$

$g(I) = \exp^{-|(\nabla * G)(I)|}$

where $ I $ is image intensity and $(\nabla * G)$ is the derivative of Gaussian operator.

: See SegmentationLevelSetImageFilter and SparseFieldLevelSetImageFilter for more information on Inputs.

PARAMETERS: The method SetUseNegatiiveFeatures() can be used to switch from propagating inwards (false) versus propagting outwards (true).

This implementation allows the user to set the weights between the propagation, advection and curvature term using methods SetPropagationScaling(), SetAdvectionScaling(), SetCurvatureScaling(). In general, the larger the CurvatureScaling, the smoother the resulting contour. To follow the implementation in Caselles's paper, set the PropagationScaling to $ c $ (the inflation or ballon force) and AdvectionScaling and CurvatureScaling both to 1.0.

OUTPUTS: The filter outputs a single, scalar, real-valued image. Negative values in the output image are inside the segmentated region and positive values in the image are outside of the inside region. The zero crossings of the image correspond to the position of the level set front.

REFERENCES: L. Lorigo, O. Faugeras, W.E.L. Grimson, R. Keriven, R. Kikinis, A. Nabavi, and C.-F. Westin, Curves: Curve evolution for vessel segmentation. Medical Image Analysis, 5:195-206, 2001.

: See SparseFieldLevelSetImageFilter and SegmentationLevelSetImageFilter for more information.

See also:: SegmentationLevelSetImageFilter; CurvesLevelSetFunction; SparseFieldLevelSetImageFilter

Definition at line 104 of file itkCurvesLevelSetImageFilter.h.