ITK  4.1.0
Insight Segmentation and Registration Toolkit
Public Types | Public Member Functions | Static Public Member Functions | Static Public Attributes | Protected Member Functions | Private Member Functions | Private Attributes
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType > Class Template Reference

#include <itkBayesianClassifierImageFilter.h>

+ Inheritance diagram for itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >:
+ Collaboration diagram for itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >:

List of all members.

Public Types

typedef SmartPointer< const SelfConstPointer
typedef
Superclass::DataObjectPointer 
DataObjectPointer
typedef
ProcessObject::DataObjectPointerArraySizeType 
DataObjectPointerArraySizeType
typedef DecisionRuleType::Pointer DecisionRulePointer
typedef
Statistics::MaximumDecisionRule 
DecisionRuleType
typedef itk::Image
< TPosteriorsPrecisionType,
itkGetStaticConstMacro(Dimension) > 
ExtractedComponentImageType
typedef InputImageType::RegionType ImageRegionType
typedef
ImageRegionConstIterator
< InputImageType
InputImageIteratorType
typedef
InputImageType::ConstPointer 
InputImagePointer
typedef Superclass::InputImageType InputImageType
typedef InputImageType::PixelType InputPixelType
typedef
ImageRegionConstIterator
< MembershipImageType
MembershipImageIteratorType
typedef
MembershipImageType::Pointer 
MembershipImagePointer
typedef TInputVectorImage MembershipImageType
typedef
MembershipImageType::PixelType 
MembershipPixelType
typedef ImageRegionIterator
< OutputImageType
OutputImageIteratorType
typedef OutputImageType::Pointer OutputImagePointer
typedef Image< TLabelsType,
itkGetStaticConstMacro(Dimension) > 
OutputImageType
typedef OutputImageType::PixelType OutputPixelType
typedef SmartPointer< SelfPointer
typedef ImageRegionIterator
< PosteriorsImageType
PosteriorsImageIteratorType
typedef
PosteriorsImageType::Pointer 
PosteriorsImagePointer
typedef VectorImage
< TPosteriorsPrecisionType,
itkGetStaticConstMacro(Dimension) > 
PosteriorsImageType
typedef
PosteriorsImageType::PixelType 
PosteriorsPixelType
typedef
ImageRegionConstIterator
< PriorsImageType
PriorsImageIteratorType
typedef PriorsImageType::Pointer PriorsImagePointer
typedef VectorImage
< TPriorsPrecisionType,
itkGetStaticConstMacro(Dimension) > 
PriorsImageType
typedef PriorsImageType::PixelType PriorsPixelType
typedef
BayesianClassifierImageFilter 
Self
typedef
SmoothingFilterType::Pointer 
SmoothingFilterPointer
typedef ImageToImageFilter
< ExtractedComponentImageType,
ExtractedComponentImageType
SmoothingFilterType
typedef ImageToImageFilter
< TInputVectorImage, Image
< TLabelsType,::itk::GetImageDimension
< TInputVectorImage >
::ImageDimension > > 
Superclass

Public Member Functions

virtual ::itk::LightObject::Pointer CreateAnother (void) const
virtual const char * GetNameOfClass () const
virtual SmoothingFilterPointer GetSmoothingFilter () const
virtual DataObjectPointer MakeOutput (DataObjectPointerArraySizeType idx)
virtual void SetPriors (const PriorsImageType *)
void SetSmoothingFilter (SmoothingFilterType *)
 typedef (Concept::Convertible< unsigned int, TLabelsType >) UnsignedIntConvertibleToLabelsCheck
 typedef (Concept::AdditiveOperators< TPosteriorsPrecisionType >) PosteriorsAdditiveOperatorsCheck
 typedef (Concept::Convertible< int, TPosteriorsPrecisionType >) IntConvertibleToPosteriorsCheck
 typedef (Concept::HasNumericTraits< typename InputPixelType::ValueType >) InputHasNumericTraitsCheck
 typedef (Concept::HasNumericTraits< TPosteriorsPrecisionType >) PosteriorsHasNumericTraitsCheck
 typedef (Concept::HasNumericTraits< TPriorsPrecisionType >) PriorsHasNumericTraitsCheck
 typedef (Concept::MultiplyOperator< typename InputPixelType::ValueType, PriorsPixelType, PosteriorsPixelType >) InputPriorsPosteriorsMultiplyOperatorCheck
virtual void SetNumberOfSmoothingIterations (unsigned int _arg)
virtual unsigned int GetNumberOfSmoothingIterations () const

Static Public Member Functions

static Pointer New ()

Static Public Attributes

static const unsigned int Dimension = ::itk::GetImageDimension< InputImageType >::ImageDimension

Protected Member Functions

 BayesianClassifierImageFilter ()
virtual void ClassifyBasedOnPosteriors ()
virtual void ComputeBayesRule ()
virtual void GenerateData ()
virtual void GenerateOutputInformation (void)
PosteriorsImageTypeGetPosteriorImage ()
virtual void NormalizeAndSmoothPosteriors ()
void PrintSelf (std::ostream &os, Indent indent) const
virtual ~BayesianClassifierImageFilter ()

Private Member Functions

 BayesianClassifierImageFilter (const Self &)
void operator= (const Self &)

Private Attributes

unsigned int m_NumberOfSmoothingIterations
SmoothingFilterPointer m_SmoothingFilter
bool m_UserProvidedPriors
bool m_UserProvidedSmoothingFilter

Detailed Description

template<class TInputVectorImage, class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
class itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >

Performs Bayesian Classification on an image.

Inputs and Outputs
The input to this filter is an itk::VectorImage that represents pixel memberships to 'n' classes. This image is conveniently generated by the BayesianClassifierInitializationImageFilter. You may use that filter to generate the membership images or specify your own.
The output of the filter is a label map (an image of unsigned char's is the default.) with pixel values indicating the classes they correspond to. Pixels with intensity 0 belong to the 0th class, 1 belong to the 1st class etc.... The classification is done by applying a Maximum decision rule to the posterior image.
Parameters
The filter optionally allows you to specify a prior image as well. The prior image, if specified must be a VectorImage with as many components as the number of classes. The posterior image is then generated by multiplying the prior image with the membership image. If the prior image is not specified, the posterior image is the same as the membership image. Another way to look at it is that the priors default to having a uniform distribution over the number of classes. Posterior membership of a pixel = Prior * Membership
The filter optionally accepts a smoothing filter and number of iterations associated with the smoothing filter. The philosophy is that the filter allows you to iteratively smooth the posteriors prior to applying the decision rule. It is hoped that this would yield a better classification. The user will need to plug in his own smoothing filter with all the parameters set.
Template parameters
InputVectorImage, datatype of the output labelmap, precision of the posterior image, precision of the prior image.
Author:
John Melonakos, Georgia Tech
Note:
This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.
See also:
VectorImage
BayesianClassifierInitializationImageFilter

Definition at line 81 of file itkBayesianClassifierImageFilter.h.


Member Typedef Documentation

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef SmartPointer< const Self > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::ConstPointer
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef Superclass::DataObjectPointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::DataObjectPointer
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef ProcessObject::DataObjectPointerArraySizeType itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::DataObjectPointerArraySizeType

This is overloaded to create the Posteriors output image

Reimplemented from itk::ImageSource< Image< TLabelsType,::itk::GetImageDimension< TInputVectorImage >::ImageDimension > >.

Definition at line 181 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef DecisionRuleType::Pointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::DecisionRulePointer

Definition at line 155 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef Statistics::MaximumDecisionRule itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::DecisionRuleType

Decision rule to use for defining the label

Definition at line 154 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef itk::Image< TPosteriorsPrecisionType, itkGetStaticConstMacro(Dimension) > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::ExtractedComponentImageType

An image from a single component of the Posterior

Definition at line 161 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef InputImageType::RegionType itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::ImageRegionType

Definition at line 115 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef ImageRegionConstIterator< InputImageType > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::InputImageIteratorType

Input and Output image iterators

Definition at line 118 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef InputImageType::ConstPointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::InputImagePointer
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef Superclass::InputImageType itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::InputImageType
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef InputImageType::PixelType itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::InputPixelType

Pixel types.

Definition at line 122 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef ImageRegionConstIterator< MembershipImageType > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::MembershipImageIteratorType

Definition at line 141 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef MembershipImageType::Pointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::MembershipImagePointer

Definition at line 140 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef TInputVectorImage itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::MembershipImageType

Image Type and Pixel type for the images representing the membership of a pixel to a particular class. This image has arrays as pixels, the number of elements in the array is the same as the number of classes to be used.

Definition at line 138 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef MembershipImageType::PixelType itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::MembershipPixelType

Definition at line 139 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef ImageRegionIterator< OutputImageType > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::OutputImageIteratorType

Definition at line 119 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef OutputImageType::Pointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::OutputImagePointer
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef Image< TLabelsType, itkGetStaticConstMacro(Dimension) > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::OutputImageType
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef OutputImageType::PixelType itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::OutputPixelType

Definition at line 123 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef SmartPointer< Self > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::Pointer
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef ImageRegionIterator< PosteriorsImageType > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::PosteriorsImageIteratorType

Definition at line 151 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef PosteriorsImageType::Pointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::PosteriorsImagePointer

Definition at line 150 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef VectorImage< TPosteriorsPrecisionType, itkGetStaticConstMacro(Dimension) > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::PosteriorsImageType

Image Type and Pixel type for the images representing the Posterior probability of a pixel belonging to a particular class. This image has arrays as pixels, the number of elements in the array is the same as the number of classes to be used.

Definition at line 148 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef PosteriorsImageType::PixelType itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::PosteriorsPixelType

Definition at line 149 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef ImageRegionConstIterator< PriorsImageType > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::PriorsImageIteratorType

Definition at line 133 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef PriorsImageType::Pointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::PriorsImagePointer

Definition at line 132 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef VectorImage< TPriorsPrecisionType, itkGetStaticConstMacro(Dimension) > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::PriorsImageType

Image Type and Pixel type for the images representing the Prior probability of a pixel belonging to a particular class. This image has arrays as pixels, the number of elements in the array is the same as the number of classes to be used.

Definition at line 130 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef PriorsImageType::PixelType itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::PriorsPixelType

Definition at line 131 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef BayesianClassifierImageFilter itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::Self
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef SmoothingFilterType::Pointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::SmoothingFilterPointer

Definition at line 168 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef ImageToImageFilter< ExtractedComponentImageType, ExtractedComponentImageType > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::SmoothingFilterType

Optional Smoothing filter that will be applied to the Posteriors

Definition at line 166 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
typedef ImageToImageFilter< TInputVectorImage, Image< TLabelsType, ::itk::GetImageDimension< TInputVectorImage >::ImageDimension > > itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::Superclass

Constructor & Destructor Documentation

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::BayesianClassifierImageFilter ( ) [protected]

End concept checking

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::~BayesianClassifierImageFilter ( ) [inline, protected, virtual]

Definition at line 212 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::BayesianClassifierImageFilter ( const Self ) [private]

Member Function Documentation

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::ClassifyBasedOnPosteriors ( ) [protected, virtual]
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::ComputeBayesRule ( ) [protected, virtual]

Methods for computing the labeled map for all combinations of conditions

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual::itk::LightObject::Pointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::CreateAnother ( void  ) const [virtual]

Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class.

Reimplemented from itk::Object.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::GenerateData ( ) [protected, virtual]

Here is where the classification is computed.

Reimplemented from itk::ImageSource< Image< TLabelsType,::itk::GetImageDimension< TInputVectorImage >::ImageDimension > >.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::GenerateOutputInformation ( void  ) [protected, virtual]

Generate the information decribing the output data. The default implementation of this method will copy information from the input to the output. A filter may override this method if its output will have different information than its input. For instance, a filter that shrinks an image will need to provide an implementation for this method that changes the spacing of the pixels. Such filters should call their superclass' implementation of this method prior to changing the information values they need (i.e. GenerateOutputInformation() should call Superclass::GenerateOutputInformation() prior to changing the information.

Reimplemented from itk::ProcessObject.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual const char* itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::GetNameOfClass ( ) const [virtual]
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual unsigned int itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::GetNumberOfSmoothingIterations ( ) const [virtual]

Number of iterations to apply the smoothing filter

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
PosteriorsImageType* itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::GetPosteriorImage ( ) [protected]
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual SmoothingFilterPointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::GetSmoothingFilter ( ) const [virtual]
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual DataObjectPointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::MakeOutput ( DataObjectPointerArraySizeType  idx) [virtual]

Make a DataObject of the correct type to used as the specified output. Every ProcessObject subclass must be able to create a DataObject that can be used as a specified output. This method is automatically called when DataObject::DisconnectPipeline() is called. DataObject::DisconnectPipeline, disconnects a data object from being an output of its current source. When the data object is disconnected, the ProcessObject needs to construct a replacement output data object so that the ProcessObject is in a valid state. So DataObject::DisconnectPipeline eventually calls ProcessObject::MakeOutput. Note that MakeOutput always returns a itkSmartPointer to a DataObject. ImageSource and MeshSource override this method to create the correct type of image and mesh respectively. If a filter has multiple outputs of different types, then that filter must provide an implementation of MakeOutput().

Reimplemented from itk::ImageSource< Image< TLabelsType,::itk::GetImageDimension< TInputVectorImage >::ImageDimension > >.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
static Pointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::New ( ) [static]

Method for creation through the object factory.

Reimplemented from itk::Object.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::NormalizeAndSmoothPosteriors ( ) [protected, virtual]
template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::operator= ( const Self ) [private]

PushBackInput(), PushFronInput() in the public section force the input to be the type expected by an ImageToImageFilter. However, these methods end of "hiding" the versions from the superclass (ProcessObject) whose arguments are DataObjects. Here, we re-expose the versions from ProcessObject to avoid warnings about hiding methods from the superclass.

Reimplemented from itk::ImageToImageFilter< TInputVectorImage, Image< TLabelsType,::itk::GetImageDimension< TInputVectorImage >::ImageDimension > >.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::PrintSelf ( std::ostream &  os,
Indent  indent 
) const [protected, virtual]

Methods invoked by Print() to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes.

Reimplemented from itk::ImageToImageFilter< TInputVectorImage, Image< TLabelsType,::itk::GetImageDimension< TInputVectorImage >::ImageDimension > >.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::SetNumberOfSmoothingIterations ( unsigned int  _arg) [virtual]

Number of iterations to apply the smoothing filter

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
virtual void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::SetPriors ( const PriorsImageType ) [virtual]

Set the priors

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
void itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::SetSmoothingFilter ( SmoothingFilterType )

Set/ Get macros for the smoothing filter that may optionally be applied to the posterior image

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::typedef ( Concept::Convertible< unsigned int, TLabelsType >  )

Begin concept checking This class requires UnsignedIntConvertibleToLabelsCheck in the form of ( Concept::Convertible< unsigned int, TLabelsType > )

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::typedef ( Concept::AdditiveOperators< TPosteriorsPrecisionType >  )

This class requires PosteriorsAdditiveOperatorsCheck in the form of ( Concept::AdditiveOperators< TPosteriorsPrecisionType > )

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::typedef ( Concept::Convertible< int, TPosteriorsPrecisionType >  )

This class requires IntConvertibleToPosteriorsCheck in the form of ( Concept::Convertible< int, TPosteriorsPrecisionType > )

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::typedef ( Concept::HasNumericTraits< typename InputPixelType::ValueType >  )

This class requires InputHasNumericTraitsCheck in the form of ( Concept::HasNumericTraits< typename InputPixelType::ValueType > )

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::typedef ( Concept::HasNumericTraits< TPosteriorsPrecisionType >  )

This class requires PosteriorsHasNumericTraitsCheck in the form of ( Concept::HasNumericTraits< TPosteriorsPrecisionType > )

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::typedef ( Concept::HasNumericTraits< TPriorsPrecisionType >  )

This class requires PriorsHasNumericTraitsCheck in the form of ( Concept::HasNumericTraits< TPriorsPrecisionType > )

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::typedef ( Concept::MultiplyOperator< typename InputPixelType::ValueType, PriorsPixelType, PosteriorsPixelType )

This class requires InputPriorsPosteriorsMultiplyOperatorCheck in the form of ( Concept::MultiplyOperator< typename InputPixelType::ValueType, PriorsPixelType, PosteriorsPixelType > )


Member Data Documentation

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
const unsigned int itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::Dimension = ::itk::GetImageDimension< InputImageType >::ImageDimension [static]

Dimension of the input image

Definition at line 109 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
unsigned int itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::m_NumberOfSmoothingIterations [private]

Number of iterations to apply the smoothing filter

Definition at line 245 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
SmoothingFilterPointer itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::m_SmoothingFilter [private]

Pointer to optional Smoothing filter

Definition at line 242 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
bool itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::m_UserProvidedPriors [private]

Boolean flag indicating that the user defined the Priors optional input

Definition at line 236 of file itkBayesianClassifierImageFilter.h.

template<class TInputVectorImage , class TLabelsType = unsigned char, class TPosteriorsPrecisionType = double, class TPriorsPrecisionType = double>
bool itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >::m_UserProvidedSmoothingFilter [private]

Boolean flag indicating that the user provided a Smoothing filter

Definition at line 239 of file itkBayesianClassifierImageFilter.h.


The documentation for this class was generated from the following file: