SimpleITK  2.0.0rc2.post38
Public Types | Public Member Functions | Private Types | Private Member Functions | Private Attributes | Friends | List of all members
itk::simple::MultiLabelSTAPLEImageFilter Class Reference

This filter performs a pixelwise combination of an arbitrary number of input images, where each of them represents a segmentation of the same scene (i.e., image). More...

#include <sitkMultiLabelSTAPLEImageFilter.h>

+ Inheritance diagram for itk::simple::MultiLabelSTAPLEImageFilter:
+ Collaboration diagram for itk::simple::MultiLabelSTAPLEImageFilter:

Public Types

using PixelIDTypeList = UnsignedIntegerPixelIDTypeList
 
using Self = MultiLabelSTAPLEImageFilter
 
- Public Types inherited from itk::simple::ImageFilter
using Self = ImageFilter
 
- Public Types inherited from itk::simple::ProcessObject
using Self = ProcessObject
 

Public Member Functions

Image Execute (const Image &image1)
 
Image Execute (const Image &image1, const Image &image2)
 
Image Execute (const Image &image1, const Image &image2, const Image &image3)
 
Image Execute (const Image &image1, const Image &image2, const Image &image3, const Image &image4)
 
Image Execute (const Image &image1, const Image &image2, const Image &image3, const Image &image4, const Image &image5)
 
Image Execute (const std::vector< Image > &images)
 
std::vector< float > GetConfusionMatrix (unsigned int input) const
 
uint64_t GetLabelForUndecidedPixels () const
 
unsigned int GetMaximumNumberOfIterations () const
 
std::string GetName () const
 
std::vector< float > GetPriorProbabilities () const
 
float GetTerminationUpdateThreshold () const
 
 MultiLabelSTAPLEImageFilter ()
 
SelfSetLabelForUndecidedPixels (uint64_t LabelForUndecidedPixels)
 
SelfSetMaximumNumberOfIterations (unsigned int MaximumNumberOfIterations)
 
SelfSetPriorProbabilities (std::vector< float > PriorProbabilities)
 
SelfSetTerminationUpdateThreshold (float TerminationUpdateThreshold)
 
std::string ToString () const
 
virtual ~MultiLabelSTAPLEImageFilter ()
 
- Public Member Functions inherited from itk::simple::ImageFilter
 ImageFilter ()
 
virtual ~ImageFilter ()=0
 
- Public Member Functions inherited from itk::simple::ProcessObject
virtual void Abort ()
 
virtual int AddCommand (itk::simple::EventEnum event, const std::function< void()> &func)
 Directly add a callback to observe an event. More...
 
virtual int AddCommand (itk::simple::EventEnum event, itk::simple::Command &cmd)
 Add a Command Object to observer the event. More...
 
virtual float GetProgress () const
 An Active Measurement of the progress of execution. More...
 
virtual bool HasCommand (itk::simple::EventEnum event) const
 Query of this object has any registered commands for event. More...
 
 ProcessObject ()
 
virtual void RemoveAllCommands ()
 Remove all registered commands. More...
 
virtual ~ProcessObject ()
 
virtual void DebugOn ()
 
virtual void DebugOff ()
 
virtual bool GetDebug () const
 
virtual void SetDebug (bool debugFlag)
 
virtual void SetNumberOfThreads (unsigned int n)
 
virtual unsigned int GetNumberOfThreads () const
 
virtual void SetNumberOfWorkUnits (unsigned int n)
 
virtual unsigned int GetNumberOfWorkUnits () const
 

Private Types

using MemberFunctionType = Image(Self::*)(const std::vector< Image > &)
 

Private Member Functions

template<class TImageType >
Image ExecuteInternal (const std::vector< Image > &images)
 

Private Attributes

itk::ProcessObjectm_Filter {nullptr}
 
uint64_t m_LabelForUndecidedPixels {std::numeric_limits<uint64_t>::max()}
 
unsigned int m_MaximumNumberOfIterations {std::numeric_limits<unsigned int>::max()}
 
std::unique_ptr< detail::MemberFunctionFactory< MemberFunctionType > > m_MemberFactory
 
std::function< std::vector< float >unsigned int)> m_pfGetConfusionMatrix
 
std::vector< float > m_PriorProbabilities {std::vector<float>()}
 
float m_TerminationUpdateThreshold {1e-5f}
 

Friends

struct detail::MemberFunctionAddressor< MemberFunctionType >
 

Additional Inherited Members

- Static Public Member Functions inherited from itk::simple::ProcessObject
static bool GetGlobalDefaultDebug ()
 
static void GlobalDefaultDebugOff ()
 
static void GlobalDefaultDebugOn ()
 
static void SetGlobalDefaultDebug (bool debugFlag)
 
static void GlobalWarningDisplayOn ()
 
static void GlobalWarningDisplayOff ()
 
static void SetGlobalWarningDisplay (bool flag)
 
static bool GetGlobalWarningDisplay ()
 
static double GetGlobalDefaultCoordinateTolerance ()
 Access the global tolerance to determine congruent spaces. More...
 
static void SetGlobalDefaultCoordinateTolerance (double)
 Access the global tolerance to determine congruent spaces. More...
 
static double GetGlobalDefaultDirectionTolerance ()
 Access the global tolerance to determine congruent spaces. More...
 
static void SetGlobalDefaultDirectionTolerance (double)
 Access the global tolerance to determine congruent spaces. More...
 
static bool SetGlobalDefaultThreader (const std::string &threader)
 Set/Get the default threader used for process objects. More...
 
static std::string GetGlobalDefaultThreader ()
 Set/Get the default threader used for process objects. More...
 
static void SetGlobalDefaultNumberOfThreads (unsigned int n)
 
static unsigned int GetGlobalDefaultNumberOfThreads ()
 Set/Get the default threader used for process objects. More...
 
- Protected Member Functions inherited from itk::simple::ImageFilter
void CheckImageMatchingDimension (const Image &image1, const Image &image2, const std::string &image2Name)
 
void CheckImageMatchingPixelType (const Image &image1, const Image &image2, const std::string &image2Name)
 
void CheckImageMatchingSize (const Image &image1, const Image &image2, const std::string &image2Name)
 
- Protected Member Functions inherited from itk::simple::ProcessObject
virtual unsigned long AddITKObserver (const itk::EventObject &, itk::Command *)
 
virtual itk::ProcessObjectGetActiveProcess ()
 
virtual void OnActiveProcessDelete ()
 
virtual void onCommandDelete (const itk::simple::Command *cmd) noexcept
 
virtual void PreUpdate (itk::ProcessObject *p)
 
virtual void RemoveITKObserver (EventCommand &e)
 
- Protected Member Functions inherited from itk::simple::NonCopyable
 NonCopyable ()=default
 
 NonCopyable (const NonCopyable &)=delete
 
NonCopyableoperator= (const NonCopyable &)=delete
 
- Static Protected Member Functions inherited from itk::simple::ImageFilter
template<class TImageType >
static void FixNonZeroIndex (TImageType *img)
 
- Static Protected Member Functions inherited from itk::simple::ProcessObject
template<class TImageType >
static TImageType::ConstPointer CastImageToITK (const Image &img)
 
template<class TPixelType , unsigned int VImageDimension, unsigned int VLength, template< typename, unsigned int > class TVector>
static Image CastITKToImage (itk::Image< TVector< TPixelType, VLength >, VImageDimension > *img)
 
template<class TImageType >
static Image CastITKToImage (TImageType *img)
 
static const itk::EventObjectGetITKEventObject (EventEnum e)
 
template<typename T >
static std::ostream & ToStringHelper (std::ostream &os, const T &v)
 
static std::ostream & ToStringHelper (std::ostream &os, const char &v)
 
static std::ostream & ToStringHelper (std::ostream &os, const signed char &v)
 
static std::ostream & ToStringHelper (std::ostream &os, const unsigned char &v)
 

Detailed Description

This filter performs a pixelwise combination of an arbitrary number of input images, where each of them represents a segmentation of the same scene (i.e., image).

The labelings in the images are weighted relative to each other based on their "performance" as estimated by an expectation-maximization algorithm. In the process, a ground truth segmentation is estimated, and the estimated performances of the individual segmentations are relative to this estimated ground truth.

The algorithm is based on the binary STAPLE algorithm by Warfield et al. as published originally in

S. Warfield, K. Zou, W. Wells, "Validation of image segmentation and expert quality with an expectation-maximization algorithm" in MICCAI 2002: Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag, Heidelberg, Germany, 2002, pp. 298-306

The multi-label algorithm implemented here is described in detail in

T. Rohlfing, D. B. Russakoff, and C. R. Maurer, Jr., "Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation," IEEE Transactions on Medical Imaging, vol. 23, pp. 983-994, Aug. 2004.

INPUTS
All input volumes to this filter must be segmentations of an image, that is, they must have discrete pixel values where each value represents a different segmented object.

Input volumes must all contain the same size RequestedRegions. Not all input images must contain all possible labels, but all label values must have the same meaning in all images.

The filter can optionally be provided with estimates for the a priori class probabilities through the SetPriorProbabilities function. If no estimate is provided, one is automatically generated by analyzing the relative frequencies of the labels in the input images.

OUTPUTS
The filter produces a single output volume. Each output pixel contains the label that has the highest probability of being the correct label, based on the performance models of the individual segmentations. If the maximum probability is not unique, i.e., if more than one label have a maximum probability, then an "undecided" label is assigned to that output pixel.

By default, the label used for undecided pixels is the maximum label value used in the input images plus one. Since it is possible for an image with 8 bit pixel values to use all 256 possible label values, it is permissible to combine 8 bit (i.e., byte) images into a 16 bit (i.e., short) output image.

In addition to the combined image, the estimated confusion matrices for each of the input segmentations can be obtained through the GetConfusionMatrix member function.

PARAMETERS
The label used for "undecided" labels can be set using SetLabelForUndecidedPixels. This functionality can be unset by calling UnsetLabelForUndecidedPixels.

A termination threshold for the EM iteration can be defined by calling SetTerminationUpdateThreshold. The iteration terminates once no single parameter of any confusion matrix changes by less than this threshold. Alternatively, a maximum number of iterations can be specified by calling SetMaximumNumberOfIterations. The algorithm may still terminate after a smaller number of iterations if the termination threshold criterion is satisfied.

EVENTS
This filter invokes IterationEvent() at each iteration of the E-M algorithm. Setting the AbortGenerateData() flag will cause the algorithm to halt after the current iteration and produce results just as if it had converged. The algorithm makes no attempt to report its progress since the number of iterations needed cannot be known in advance.
Author
Torsten Rohlfing, SRI International, Neuroscience Program
See also
itk::simple::MultiLabelSTAPLE for the procedural interface

Definition at line 80 of file sitkMultiLabelSTAPLEImageFilter.h.

Member Typedef Documentation

◆ MemberFunctionType

using itk::simple::MultiLabelSTAPLEImageFilter::MemberFunctionType = Image (Self::*)( const std::vector<Image> & )
private

Setup for member function dispatching

Definition at line 176 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ PixelIDTypeList

Define the pixels types supported by this filter

Definition at line 95 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ Self

Definition at line 84 of file sitkMultiLabelSTAPLEImageFilter.h.

Constructor & Destructor Documentation

◆ ~MultiLabelSTAPLEImageFilter()

virtual itk::simple::MultiLabelSTAPLEImageFilter::~MultiLabelSTAPLEImageFilter ( )
virtual

Destructor

◆ MultiLabelSTAPLEImageFilter()

itk::simple::MultiLabelSTAPLEImageFilter::MultiLabelSTAPLEImageFilter ( )

Default Constructor that takes no arguments and initializes default parameters

Member Function Documentation

◆ Execute() [1/6]

Image itk::simple::MultiLabelSTAPLEImageFilter::Execute ( const Image image1)

◆ Execute() [2/6]

Image itk::simple::MultiLabelSTAPLEImageFilter::Execute ( const Image image1,
const Image image2 
)

◆ Execute() [3/6]

Image itk::simple::MultiLabelSTAPLEImageFilter::Execute ( const Image image1,
const Image image2,
const Image image3 
)

◆ Execute() [4/6]

Image itk::simple::MultiLabelSTAPLEImageFilter::Execute ( const Image image1,
const Image image2,
const Image image3,
const Image image4 
)

◆ Execute() [5/6]

Image itk::simple::MultiLabelSTAPLEImageFilter::Execute ( const Image image1,
const Image image2,
const Image image3,
const Image image4,
const Image image5 
)

◆ Execute() [6/6]

Image itk::simple::MultiLabelSTAPLEImageFilter::Execute ( const std::vector< Image > &  images)

Execute the filter on the input images

◆ ExecuteInternal()

template<class TImageType >
Image itk::simple::MultiLabelSTAPLEImageFilter::ExecuteInternal ( const std::vector< Image > &  images)
private

◆ GetConfusionMatrix()

std::vector<float> itk::simple::MultiLabelSTAPLEImageFilter::GetConfusionMatrix ( unsigned int  input) const
inline

Get confusion matrix for the i-th input segmentation.

This is an active measurement. It may be accessed while the filter is being executing in command call-backs and can be accessed after execution.

Definition at line 152 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ GetLabelForUndecidedPixels()

uint64_t itk::simple::MultiLabelSTAPLEImageFilter::GetLabelForUndecidedPixels ( ) const
inline
     Get label value used for undecided pixels.

After updating the filter, this function returns the actual label value used for undecided pixels in the current output. Note that this value is overwritten when SetLabelForUndecidedPixels is called and the new value only becomes effective upon the next filter update.

Definition at line 110 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ GetMaximumNumberOfIterations()

unsigned int itk::simple::MultiLabelSTAPLEImageFilter::GetMaximumNumberOfIterations ( ) const
inline

Set maximum number of iterations.

Definition at line 130 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ GetName()

std::string itk::simple::MultiLabelSTAPLEImageFilter::GetName ( ) const
inlinevirtual

Name of this class

Implements itk::simple::ProcessObject.

Definition at line 156 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ GetPriorProbabilities()

std::vector<float> itk::simple::MultiLabelSTAPLEImageFilter::GetPriorProbabilities ( ) const
inline
     Get prior class probabilities.

After updating the filter, this function returns the actual prior class probabilities. If these were not previously set by a call to SetPriorProbabilities, then they are estimated from the input segmentations and the result is available through this function.

Definition at line 144 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ GetTerminationUpdateThreshold()

float itk::simple::MultiLabelSTAPLEImageFilter::GetTerminationUpdateThreshold ( ) const
inline

Set termination threshold based on confusion matrix parameter updates.

Definition at line 120 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ SetLabelForUndecidedPixels()

Self& itk::simple::MultiLabelSTAPLEImageFilter::SetLabelForUndecidedPixels ( uint64_t  LabelForUndecidedPixels)
inline

Set label value for undecided pixels.

Definition at line 103 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ SetMaximumNumberOfIterations()

Self& itk::simple::MultiLabelSTAPLEImageFilter::SetMaximumNumberOfIterations ( unsigned int  MaximumNumberOfIterations)
inline

Set maximum number of iterations.

Definition at line 125 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ SetPriorProbabilities()

Self& itk::simple::MultiLabelSTAPLEImageFilter::SetPriorProbabilities ( std::vector< float >  PriorProbabilities)
inline
     Set manual estimates for the a priori class probabilities. The size of the array must be greater than the value of the

largest label. The index into the array corresponds to the label value in the segmented image for the class.

Definition at line 137 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ SetTerminationUpdateThreshold()

Self& itk::simple::MultiLabelSTAPLEImageFilter::SetTerminationUpdateThreshold ( float  TerminationUpdateThreshold)
inline

Set termination threshold based on confusion matrix parameter updates.

Definition at line 115 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ ToString()

std::string itk::simple::MultiLabelSTAPLEImageFilter::ToString ( ) const
virtual

Print ourselves out

Reimplemented from itk::simple::ProcessObject.

Friends And Related Function Documentation

◆ detail::MemberFunctionAddressor< MemberFunctionType >

Definition at line 181 of file sitkMultiLabelSTAPLEImageFilter.h.

Member Data Documentation

◆ m_Filter

itk::ProcessObject* itk::simple::MultiLabelSTAPLEImageFilter::m_Filter {nullptr}
private

Definition at line 198 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ m_LabelForUndecidedPixels

uint64_t itk::simple::MultiLabelSTAPLEImageFilter::m_LabelForUndecidedPixels {std::numeric_limits<uint64_t>::max()}
private

Definition at line 186 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ m_MaximumNumberOfIterations

unsigned int itk::simple::MultiLabelSTAPLEImageFilter::m_MaximumNumberOfIterations {std::numeric_limits<unsigned int>::max()}
private

Definition at line 190 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ m_MemberFactory

std::unique_ptr<detail::MemberFunctionFactory<MemberFunctionType> > itk::simple::MultiLabelSTAPLEImageFilter::m_MemberFactory
private

Definition at line 183 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ m_pfGetConfusionMatrix

std::function<std::vector<float>unsigned int)> itk::simple::MultiLabelSTAPLEImageFilter::m_pfGetConfusionMatrix
private

Definition at line 195 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ m_PriorProbabilities

std::vector<float> itk::simple::MultiLabelSTAPLEImageFilter::m_PriorProbabilities {std::vector<float>()}
private

Definition at line 192 of file sitkMultiLabelSTAPLEImageFilter.h.

◆ m_TerminationUpdateThreshold

float itk::simple::MultiLabelSTAPLEImageFilter::m_TerminationUpdateThreshold {1e-5f}
private

Definition at line 188 of file sitkMultiLabelSTAPLEImageFilter.h.


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