This filter is an implementation of a Canny edge detector for scalar-valued images. Based on John Canny's paper "A Computational Approach to Edge Detection"(IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No.6, November 1986), there are four major steps used in the edge-detection scheme: (1) Smooth the input image with Gaussian filter. (2) Calculate the second directional derivatives of the smoothed image. (3) Non-Maximum Suppression: the zero-crossings of 2nd derivative are found, and the sign of third derivative is used to find the correct extrema. (4) The hysteresis thresholding is applied to the gradient magnitude (multiplied with zero-crossings) of the smoothed image to find and link edges. More...

#include <itkCannyEdgeDetectionImageFilter.h>

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

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

List of all members.

Classes
struct	CannyThreadStruct
Public Types
typedef FixedArray< double, itkGetStaticConstMacro(ImageDimension) >	ArrayType
typedef SmartPointer< const Self >	ConstPointer
typedef ZeroFluxNeumannBoundaryCondition < OutputImageType >	DefaultBoundaryConditionType
typedef TInputImage::IndexType	IndexType
typedef TInputImage::PixelType	InputImagePixelType
typedef TInputImage::RegionType	InputImageRegionType
typedef TInputImage	InputImageType
typedef ObjectStore< ListNodeType >	ListNodeStorageType
typedef ListNode< IndexType >	ListNodeType
typedef ListType::Pointer	ListPointerType
typedef SparseFieldLayer < ListNodeType >	ListType
typedef ConstNeighborhoodIterator < OutputImageType, DefaultBoundaryConditionType >	NeighborhoodType
typedef TOutputImage::PixelType	OutputImagePixelType
typedef TOutputImage::RegionType	OutputImageRegionType
typedef TOutputImage	OutputImageType
typedef SmartPointer< Self >	Pointer
typedef CannyEdgeDetectionImageFilter	Self
typedef TInputImage::SizeValueType	SizeValueType
typedef ImageToImageFilter < TInputImage, TOutputImage >	Superclass
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
virtual void	GenerateInputRequestedRegion () throw ( InvalidRequestedRegionError )
virtual OutputImagePixelType	GetLowerThreshold () const
virtual const char *	GetNameOfClass () const
OutputImageType *	GetNonMaximumSuppressionImage ()
virtual OutputImagePixelType	GetUpperThreshold () const
virtual void	SetLowerThreshold (OutputImagePixelType _arg)
virtual void	SetUpperThreshold (OutputImagePixelType _arg)
	typedef (Concept::HasNumericTraits< OutputImagePixelType >) OutputHasNumericTraitsCheck
	typedef (Concept::HasNumericTraits< InputImagePixelType >) InputHasNumericTraitsCheck
	typedef (Concept::SameDimension< ImageDimension, OutputImageDimension >) SameDimensionCheck
	typedef (Concept::IsFloatingPoint< InputImagePixelType >) InputIsFloatingPointCheck
	typedef (Concept::IsFloatingPoint< OutputImagePixelType >) OutputIsFloatingPointCheck

virtual void	SetVariance (ArrayType _arg)
virtual const ArrayType	GetVariance () const
virtual void	SetMaximumError (ArrayType _arg)
virtual const ArrayType	GetMaximumError () const

void	SetVariance (const typename ArrayType::ValueType v)

void	SetMaximumError (const typename ArrayType::ValueType v)
Static Public Member Functions
static Pointer	New ()
Static Public Attributes

static const unsigned int	ImageDimension = TInputImage::ImageDimension
static const unsigned int	OutputImageDimension = TOutputImage::ImageDimension
Protected Types
typedef DiscreteGaussianImageFilter < InputImageType, OutputImageType >	GaussianImageFilterType
typedef MultiplyImageFilter < OutputImageType, OutputImageType, OutputImageType >	MultiplyImageFilterType
Protected Member Functions
void	GenerateData ()

	CannyEdgeDetectionImageFilter ()
void	PrintSelf (std::ostream &os, Indent indent) const
Private Member Functions
void	AllocateUpdateBuffer ()
	CannyEdgeDetectionImageFilter (const Self &)
void	Compute2ndDerivative ()
void	Compute2ndDerivativePos ()
OutputImagePixelType	ComputeCannyEdge (const NeighborhoodType &it, void *globalData)
void	FollowEdge (IndexType index)
void	HysteresisThresholding ()
void	operator= (const Self &)
void	ThreadedCompute2ndDerivative (const OutputImageRegionType &outputRegionForThread, ThreadIdType threadId)
void	ThreadedCompute2ndDerivativePos (const OutputImageRegionType &outputRegionForThread, ThreadIdType threadId)
virtual	~CannyEdgeDetectionImageFilter ()
Static Private Member Functions
static ITK_THREAD_RETURN_TYPE	Compute2ndDerivativePosThreaderCallback (void *arg)
static ITK_THREAD_RETURN_TYPE	Compute2ndDerivativeThreaderCallback (void *arg)
Private Attributes
SizeValueType	m_Center
std::slice	m_ComputeCannyEdgeSlice [ImageDimension]
GaussianImageFilterType::Pointer	m_GaussianFilter
OutputImagePixelType	m_LowerThreshold
ArrayType	m_MaximumError
MultiplyImageFilterType::Pointer	m_MultiplyImageFilter
ListPointerType	m_NodeList
ListNodeStorageType::Pointer	m_NodeStore
SizeValueType	m_Stride [ImageDimension]
OutputImageType::Pointer	m_UpdateBuffer1
OutputImagePixelType	m_UpperThreshold
ArrayType	m_Variance

DerivativeOperator < OutputImagePixelType, itkGetStaticConstMacro(ImageDimension) >	m_ComputeCannyEdge1stDerivativeOper
DerivativeOperator < OutputImagePixelType, itkGetStaticConstMacro(ImageDimension) >	m_ComputeCannyEdge2ndDerivativeOper

Detailed Description

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

This filter is an implementation of a Canny edge detector for scalar-valued images. Based on John Canny's paper "A Computational Approach to Edge Detection"(IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No.6, November 1986), there are four major steps used in the edge-detection scheme: (1) Smooth the input image with Gaussian filter. (2) Calculate the second directional derivatives of the smoothed image. (3) Non-Maximum Suppression: the zero-crossings of 2nd derivative are found, and the sign of third derivative is used to find the correct extrema. (4) The hysteresis thresholding is applied to the gradient magnitude (multiplied with zero-crossings) of the smoothed image to find and link edges.

Inputs and Outputs: The input to this filter should be a scalar, real-valued Itk image of arbitrary dimension. The output should also be a scalar, real-value Itk image of the same dimensionality.

Parameters: There are four parameters for this filter that control the sub-filters used by the algorithm.

: Variance and Maximum error are used in the Gaussian smoothing of the input image. See itkDiscreteGaussianImageFilter for information on these parameters.

: Threshold is the lowest allowed value in the output image. Its data type is the same as the data type of the output image. Any values below the Threshold level will be replaced with the OutsideValue parameter value, whose default is zero.

Todo:: Edge-linking will be added when an itk connected component labeling algorithm is available.

See also:: DiscreteGaussianImageFilter; ZeroCrossingImageFilter; ThresholdImageFilter

Definition at line 84 of file itkCannyEdgeDetectionImageFilter.h.