#include <itkSegmentationLevelSetImageFilter.h>

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

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

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef TFeatureImage	FeatureImageType
typedef Superclass::IndexType	IndexType
typedef Superclass::InputImageType	InputImageType
typedef Image < TOutputPixelType, itkGetStaticConstMacro(InputImageDimension) >	OutputImageType
typedef SmartPointer< Self >	Pointer
typedef SegmentationLevelSetFunction < OutputImageType, FeatureImageType >	SegmentationFunctionType
typedef SegmentationLevelSetImageFilter	Self
typedef SegmentationFunctionType::ImageType	SpeedImageType
typedef SparseFieldLevelSetImageFilter < TInputImage, OutputImageType >	Superclass
typedef Superclass::TimeStepType	TimeStepType
typedef Superclass::ValueType	ValueType
typedef SegmentationFunctionType::VectorImageType	VectorImageType
Public Member Functions
void	GenerateAdvectionImage ()
void	GenerateSpeedImage ()
virtual const VectorImageType *	GetAdvectionImage () const
ValueType	GetAdvectionScaling () const
ValueType	GetCurvatureScaling () const
virtual FeatureImageType *	GetFeatureImage ()
double	GetMaximumCurvatureTimeStep () const
unsigned int	GetMaximumIterations ()
double	GetMaximumPropagationTimeStep () const
virtual const char *	GetNameOfClass () const
ValueType	GetPropagationScaling () const
virtual SegmentationFunctionType *	GetSegmentationFunction ()
virtual const SpeedImageType *	GetSpeedImage () const
bool	GetUseMinimalCurvature () const
bool	GetUseNegativeFeatures () const
void	SetAdvectionImage (VectorImageType *v)
virtual void	SetInitialImage (InputImageType *f)
void	SetInput2 (const FeatureImageType *input)
virtual void	SetSegmentationFunction (SegmentationFunctionType *s)
void	SetSpeedImage (SpeedImageType *s)
void	SetUseNegativeFeaturesOff ()
	typedef (Concept::HasNumericTraits< TOutputPixelType >) OutputHasNumericTraitsCheck
void	UseMinimalCurvatureOff ()
void	UseMinimalCurvatureOn ()

void	SetMaximumIterations (unsigned int i)

virtual void	SetFeatureImage (const FeatureImageType *f)

void	SetUseNegativeFeaturesOn ()

void	SetUseNegativeFeatures (bool u)

virtual void	SetReverseExpansionDirection (bool _arg)
virtual bool	GetReverseExpansionDirection () const
virtual void	ReverseExpansionDirectionOn ()
virtual void	ReverseExpansionDirectionOff ()

virtual void	SetAutoGenerateSpeedAdvection (bool _arg)
virtual bool	GetAutoGenerateSpeedAdvection () const
virtual void	AutoGenerateSpeedAdvectionOn ()
virtual void	AutoGenerateSpeedAdvectionOff ()

void	SetFeatureScaling (ValueType v)

void	SetPropagationScaling (ValueType v)

void	SetAdvectionScaling (ValueType v)

void	SetCurvatureScaling (ValueType v)

void	SetUseMinimalCurvature (bool b)

void	SetMaximumCurvatureTimeStep (double n)

void	SetMaximumPropagationTimeStep (double n)
Protected Member Functions
void	GenerateData ()
virtual void	PrintSelf (std::ostream &os, Indent indent) const
	SegmentationLevelSetImageFilter ()
virtual	~SegmentationLevelSetImageFilter ()

virtual void	InitializeIteration ()
Protected Attributes
bool	m_ReverseExpansionDirection
Private Attributes
SegmentationFunctionType *	m_SegmentationFunction
bool	m_AutoGenerateSpeedAdvection
	SegmentationLevelSetImageFilter (const Self &)
void	operator= (const Self &)

Detailed Description

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

A base class which defines the API for implementing a special class of image segmentation filters using level set methods.

OVERVIEW: This object defines the framework for a class of segmentation filters which use level set methods. These filters work by constructing a ``feature image'' onto which the evolving level set locks as it moves. In the feature image, values that are close to zero are associated with object boundaries. An original (or preprocessed) image is given to the filter as the feature image and a seed for the level set is given as the input of the filter. The seed is converted into a level set embedding which propagates according to the features calculated from the original image.

TEMPLATE PARAMETERS: There are two required and one optional template parameter for these filters.

TInputImage is the image type of the initial model you will input to the filter using SetInput() or SetInitialImage().

TFeatureImage is the image type of the image from which the filter will calculate the speed term for segmentation (see INPUTS).

TOutputPixelType is the data type used for the output image phi, the implicit level set image. This should really only ever be set as float (default) or double.

INPUTS: The input to any subclass of this filter is the seed image for the initial level set embedding. As with other subclasses of the SparseLevelSetImageFilter, the type of the input image is is not important. The (RequestedRegion) size of the seed image must, however, match the (RequestedRegion) size of the feature image.

You must identify the initial front (surface) in the input image. You do this by specifying its isovalue through the method SetIsoSurfaceValue(float f). The algorithm will then initialize its solution using the front represented by value f. Note that the front is always represented by isosurface zero in the output and not the isosurface you specified for the input. This is because, for simplicity, the filter will shift your input image so that the active front has zero values.

: Depending on the particular application and filter that you are using, the feature image should be preprocessed with some type of noise reduction filtering. The feature image input can be of any type, but it will be cast to floating point before calculations are done.

OUTPUTS: The output of any subclass of this filter is a level set embedding as described in SparseFieldLevelSetImageFilter. The zero crossings of the output image give the pixels closest to the level set boundary. By ITK convention, NEGATIVE values are pixels INSIDE the segmented region and POSITIVE values are pixels OUTSIDE the segmented region.

PARAMETERS: The MaximumRMSChange parameter is used to determine when the solution has converged. A lower value will result in a tighter-fitting solution, but will require more computations. Too low a value could put the solver into an infinite loop unless a reasonable NumberOfIterations parameter is set. Values should always be greater than 0.0 and less than 1.0.

: The NumberOfIterations parameter can be used to halt the solution after a specified number of iterations, overriding the MaximumRMSChange halting criteria.

: The standard convention for ITK level-set segmentation filters is that POSITIVE propagation (speed) and advection terms cause the surface to EXPAND while negative terms cause the surface to CONTRACT. When the ReverseExpansionDirection parameter is set to TRUE (on), it tells the function object to reverse the standard ITK convention so that NEGATIVE terms cause EXPANSION and positive terms cause CONTRACTION.

This parameter can be safely changed as appropriate for a particular application or data set to achieve the desired behavior.

: The FeatureScaling parameter controls the magnitude of the features calculated for use in the level set propagation and advection speeds. This value simply sets both parameters to equal values at once. Some filters may only use on of these two terms and this method is a generic way to set either or both without having to know which is in use.

: The CurvatureScaling parameter controls the magnitude of the curvature values which are calculated on the evolving isophote. This is important in controlling the relative effect of curvature in the calculation. Default value is 1.0. Higher values relative to the other level set equation terms (propagation and advection) will give a smoother result.

: The PropagationScaling parameter controls the scaling of the scalar propagation (speed) term relative to other terms in the level set equation. Setting this value will override any value already set by FeatureScaling.

: The AdvectionScaling parameter controls the scaling of the vector advection field term relative to other terms in the level set equation. Setting this value will override any value already set by FeatureScaling.

Warning:: This is an abstract class. It is not intended to be instantiated by itself. Instead, you should use the derived classes. This is the reason why the New() operator (itkNewMacro) is missing from the class API.

: See LevelSetFunction for more information.

Definition at line 145 of file itkSegmentationLevelSetImageFilter.h.