#include <itkVectorConfidenceConnectedImageFilter.h>

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

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

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef DistanceThresholdFunctionType::CovarianceMatrixType	CovarianceMatrixType
typedef DistanceThresholdFunctionType::Pointer	DistanceThresholdFunctionPointer
typedef MahalanobisDistanceThresholdImageFunction < InputImageType >	DistanceThresholdFunctionType
typedef InputImageType::IndexType	IndexType
typedef InputImageType::PixelType	InputImagePixelType
typedef InputImageType::Pointer	InputImagePointer
typedef InputImageType::RegionType	InputImageRegionType
typedef TInputImage	InputImageType
typedef DistanceThresholdFunctionType::MeanVectorType	MeanVectorType
typedef OutputImageType::PixelType	OutputImagePixelType
typedef OutputImageType::Pointer	OutputImagePointer
typedef OutputImageType::RegionType	OutputImageRegionType
typedef TOutputImage	OutputImageType
typedef SmartPointer< Self >	Pointer
typedef std::vector< IndexType >	SeedsContainerType
typedef VectorConfidenceConnectedImageFilter	Self
typedef InputImageType::SizeType	SizeType
typedef ImageToImageFilter < TInputImage, TOutputImage >	Superclass
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
const CovarianceMatrixType &	GetCovariance () const
const MeanVectorType &	GetMean () const
virtual const char *	GetNameOfClass () const
virtual const SeedsContainerType &	GetSeeds () const
void	PrintSelf (std::ostream &os, Indent indent) const
	typedef (Concept::EqualityComparable< OutputImagePixelType >) OutputEqualityComparableCheck
	typedef (Concept::HasNumericTraits< typename InputImagePixelType::ValueType >) InputHasNumericTraitsCheck
	typedef (Concept::OStreamWritable< OutputImagePixelType >) OutputOStreamWritableCheck

void	SetSeed (const IndexType &seed)

void	AddSeed (const IndexType &seed)

virtual void	SetMultiplier (double _arg)
virtual double	GetMultiplier () const

virtual void	SetNumberOfIterations (unsigned int _arg)
virtual unsigned int	GetNumberOfIterations () const

virtual void	SetReplaceValue (OutputImagePixelType _arg)
virtual OutputImagePixelType	GetReplaceValue () const

virtual void	SetInitialNeighborhoodRadius (unsigned int _arg)
virtual const unsigned int &	GetInitialNeighborhoodRadius ()
Static Public Member Functions
static Pointer	New ()
Protected Member Functions
void	EnlargeOutputRequestedRegion (DataObject *output)
void	GenerateData ()
void	GenerateInputRequestedRegion ()
	VectorConfidenceConnectedImageFilter ()
Private Member Functions
void	operator= (const Self &)
	VectorConfidenceConnectedImageFilter (const Self &)
Private Attributes
unsigned int	m_InitialNeighborhoodRadius
double	m_Multiplier
unsigned int	m_NumberOfIterations
OutputImagePixelType	m_ReplaceValue
SeedsContainerType	m_Seeds
DistanceThresholdFunctionPointer	m_ThresholdFunction

Detailed Description

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

Segment pixels with similar statistics using connectivity.

This filter extracts a connected set of pixels whose pixel intensities are consistent with the pixel statistics of a seed point. The mean and variance across a neighborhood (8-connected, 26-connected, etc.) are calculated for a seed point. Then pixels connected to this seed point whose values are within the confidence interval for the seed point are grouped. The width of the confidence interval is controlled by the "Multiplier" variable (the confidence interval is the mean plus or minus the "Multiplier" times the standard deviation). If the intensity variations across a segment were gaussian, a "Multiplier" setting of 2.5 would define a confidence interval wide enough to capture 99% of samples in the segment.

After this initial segmentation is calculated, the mean and variance are re-calculated. All the pixels in the previous segmentation are used to calculate the mean the standard deviation (as opposed to using the pixels in the neighborhood of the seed point). The segmentation is then recalculted using these refined estimates for the mean and variance of the pixel values. This process is repeated for the specified number of iterations. Setting the "NumberOfIterations" to zero stops the algorithm after the initial segmentation from the seed point.

NOTE: the lower and upper threshold are restricted to lie within the valid numeric limits of the input data pixel type. Also, the limits may be adjusted to contain the seed point's intensity.

Definition at line 59 of file itkVectorConfidenceConnectedImageFilter.h.