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::STAPLEImageFilter< TInputImage, TOutputImage > Class Template Reference

#include <itkSTAPLEImageFilter.h>

+ Inheritance diagram for itk::STAPLEImageFilter< TInputImage, TOutputImage >:
+ Collaboration diagram for itk::STAPLEImageFilter< TInputImage, TOutputImage >:

List of all members.

Public Types

typedef SmartPointer< const SelfConstPointer
typedef InputImageType::Pointer InputImagePointer
typedef TInputImage InputImageType
typedef TInputImage::PixelType InputPixelType
typedef OutputImageType::Pointer OutputImagePointer
typedef
Superclass::OutputImageRegionType 
OutputImageRegionType
typedef TOutputImage OutputImageType
typedef TOutputImage::PixelType OutputPixelType
typedef SmartPointer< SelfPointer
typedef NumericTraits
< InputPixelType >::RealType 
RealType
typedef STAPLEImageFilter Self
typedef ImageToImageFilter
< TInputImage, TOutputImage > 
Superclass

Public Member Functions

virtual ::itk::LightObject::Pointer CreateAnother (void) const
virtual unsigned int GetElapsedIterations () const
virtual const char * GetNameOfClass () const
const std::vector< double > & GetSensitivity () const
const std::vector< double > & GetSpecificity () const
 typedef (Concept::HasNumericTraits< InputPixelType >) InputHasNumericTraitsCheck
virtual void SetForegroundValue (InputPixelType _arg)
virtual InputPixelType GetForegroundValue () const
double GetSensitivity (unsigned int i)
double GetSpecificity (unsigned int i)
virtual void SetMaximumIterations (unsigned int _arg)
virtual unsigned int GetMaximumIterations () const
virtual void SetConfidenceWeight (double _arg)
virtual double GetConfidenceWeight () const

Static Public Member Functions

static Pointer New ()

Static Public Attributes

static const unsigned int ImageDimension = TOutputImage::ImageDimension

Protected Member Functions

void GenerateData ()
void PrintSelf (std::ostream &, Indent) const
virtual ~STAPLEImageFilter ()
 STAPLEImageFilter ()

Private Member Functions

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

Private Attributes

double m_ConfidenceWeight
unsigned int m_ElapsedIterations
InputPixelType m_ForegroundValue
unsigned int m_MaximumIterations
std::vector< double > m_Sensitivity
std::vector< double > m_Specificity

Detailed Description

template<typename TInputImage, typename TOutputImage>
class itk::STAPLEImageFilter< TInputImage, TOutputImage >

The STAPLE filter implements the Simultaneous Truth and Performance Level Estimation algorithm for generating ground truth volumes from a set of binary expert segmentations.

The STAPLE algorithm treats segmentation as a pixelwise classification, which leads to an averaging scheme that accounts for systematic biases in the behavior of experts in order to generate a fuzzy ground truth volume and simultaneous accuracy assessment of each expert. The ground truth volumes produced by this filter are floating point volumes of values between zero and one that indicate probability of each pixel being in the object targeted by the segmentation.

The STAPLE algorithm is described 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

INPUTS
Input volumes to the STAPLE filter must be binary segmentations of an image, that is, there must be a single foreground value that represents positively classified pixels (pixels that are considered to belong inside the segmentation). Any number of background pixel values may be present in the input images. You can, for example, input volumes with many different labels as long as the structure you are interested in creating ground truth for is consistently labeled among all input volumes. Pixel type of the input volumes does not matter. Specify the label value for positively classified pixels using SetForegroundValue. All other labels will be considered to be negatively classified pixels (background).

Input volumes must all contain the same size RequestedRegions.

OUTPUTS
The STAPLE filter produces a single output volume with a range of floating point values from zero to one. IT IS VERY IMPORTANT TO INSTANTIATE THIS FILTER WITH A FLOATING POINT OUTPUT TYPE (floats or doubles). You may threshold the output above some probability threshold if you wish to produce a binary ground truth.
PARAMETERS
The STAPLE algorithm requires a number of inputs. You may specify any number of input volumes using the SetInput(i, p_i) method, where i ranges from zero to N-1, N is the total number of input segmentations, and p_i is the SmartPointer to the i-th segmentation.

The SetConfidenceWeight parameter is a modifier for the prior probability that any pixel would be classified as inside the target object. This implementation of the STAPLE algorithm automatically calculates prior positive classification probability as the average fraction of the image volume filled by the target object in each input segmentation. The ConfidenceWeight parameter allows for scaling the of this default prior probability: if g_t is the prior probability that a pixel would be classified inside the target object, then g_t is set to g_t * ConfidenceWeight before iterating on the solution. In general ConfidenceWeight should be left to the default of 1.0.

You must provide a foreground value using SetForegroundValue that the STAPLE algorithm will use to identify positively classified pixels in the the input images. All other values in the image will be treated as background values. For example, if your input segmentations consist of 1's everywhere inside the segmented region, then use SetForegroundValue(1).

The STAPLE algorithm is an iterative E-M algorithm and will converge on a solution after some number of iterations that cannot be known a priori. After updating the filter, the total elapsed iterations taken to converge on the solution can be queried through GetElapsedIterations(). You may also specify a MaximumNumberOfIterations, after which the algorithm will stop iterating regardless of whether or not it has converged. This implementation of the STAPLE algorithm will find the solution to within seven digits of precision unless it is stopped early.

Once updated, the Sensitivity (true positive fraction, q) and Specificity (true negative fraction, q) for each expert input volume can be queried using GetSensitivity(i) and GetSpecificity(i), where i is the i-th input volume.

REQUIRED PARAMETERS
The only required parameters for this filter are the ForegroundValue and the input volumes. All other parameters may be safely left to their default values. Please see the paper cited above for more information on the STAPLE algorithm and its parameters. A proper understanding of the algorithm is important for interpreting the results that it produces.
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.

Definition at line 121 of file itkSTAPLEImageFilter.h.


Member Typedef Documentation

template<typename TInputImage , typename TOutputImage >
typedef SmartPointer< const Self > itk::STAPLEImageFilter< TInputImage, TOutputImage >::ConstPointer

Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.

Definition at line 129 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef InputImageType::Pointer itk::STAPLEImageFilter< TInputImage, TOutputImage >::InputImagePointer

Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.

Definition at line 150 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef TInputImage itk::STAPLEImageFilter< TInputImage, TOutputImage >::InputImageType

Image typedef support

Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.

Definition at line 149 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef TInputImage::PixelType itk::STAPLEImageFilter< TInputImage, TOutputImage >::InputPixelType

Definition at line 140 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef OutputImageType::Pointer itk::STAPLEImageFilter< TInputImage, TOutputImage >::OutputImagePointer

Reimplemented from itk::ImageSource< TOutputImage >.

Definition at line 152 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef Superclass::OutputImageRegionType itk::STAPLEImageFilter< TInputImage, TOutputImage >::OutputImageRegionType

Superclass typedefs.

Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.

Definition at line 155 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef TOutputImage itk::STAPLEImageFilter< TInputImage, TOutputImage >::OutputImageType

Some convenient typedefs.

Reimplemented from itk::ImageSource< TOutputImage >.

Definition at line 151 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef TOutputImage::PixelType itk::STAPLEImageFilter< TInputImage, TOutputImage >::OutputPixelType

Extract some information from the image types. Dimensionality of the two images is assumed to be the same.

Definition at line 135 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef SmartPointer< Self > itk::STAPLEImageFilter< TInputImage, TOutputImage >::Pointer

Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.

Definition at line 128 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef NumericTraits< InputPixelType >::RealType itk::STAPLEImageFilter< TInputImage, TOutputImage >::RealType

Definition at line 141 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef STAPLEImageFilter itk::STAPLEImageFilter< TInputImage, TOutputImage >::Self

Standard class typedefs.

Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.

Definition at line 126 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
typedef ImageToImageFilter< TInputImage, TOutputImage > itk::STAPLEImageFilter< TInputImage, TOutputImage >::Superclass

Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.

Definition at line 127 of file itkSTAPLEImageFilter.h.


Constructor & Destructor Documentation

template<typename TInputImage , typename TOutputImage >
itk::STAPLEImageFilter< TInputImage, TOutputImage >::STAPLEImageFilter ( ) [inline, protected]

End concept checking

Definition at line 231 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
virtual itk::STAPLEImageFilter< TInputImage, TOutputImage >::~STAPLEImageFilter ( ) [inline, protected, virtual]

Definition at line 240 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
itk::STAPLEImageFilter< TInputImage, TOutputImage >::STAPLEImageFilter ( const Self ) [private]

Member Function Documentation

template<typename TInputImage , typename TOutputImage >
virtual::itk::LightObject::Pointer itk::STAPLEImageFilter< TInputImage, TOutputImage >::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<typename TInputImage , typename TOutputImage >
void itk::STAPLEImageFilter< TInputImage, TOutputImage >::GenerateData ( ) [protected, virtual]

A version of GenerateData() specific for image processing filters. This implementation will split the processing across multiple threads. The buffer is allocated by this method. Then the BeforeThreadedGenerateData() method is called (if provided). Then, a series of threads are spawned each calling ThreadedGenerateData(). After all the threads have completed processing, the AfterThreadedGenerateData() method is called (if provided). If an image processing filter cannot be threaded, the filter should provide an implementation of GenerateData(). That implementation is responsible for allocating the output buffer. If a filter an be threaded, it should NOT provide a GenerateData() method but should provide a ThreadedGenerateData() instead.

See also:
ThreadedGenerateData()

Reimplemented from itk::ImageSource< TOutputImage >.

template<typename TInputImage , typename TOutputImage >
virtual double itk::STAPLEImageFilter< TInputImage, TOutputImage >::GetConfidenceWeight ( ) const [virtual]

Scales the estimated prior probability that a pixel will be inside the targeted object of segmentation. The default prior probability g_t is calculated automatically as the average fraction of positively classified pixels to the total size of the volume (across all input volumes). ConfidenceWeight will scale this default value as g_t = g_t * ConfidenceWeight. In general, ConfidenceWeight should be left to the default of 1.0.

template<typename TInputImage , typename TOutputImage >
virtual unsigned int itk::STAPLEImageFilter< TInputImage, TOutputImage >::GetElapsedIterations ( ) const [virtual]

Get the number of elapsed iterations of the iterative E-M algorithm.

template<typename TInputImage , typename TOutputImage >
virtual InputPixelType itk::STAPLEImageFilter< TInputImage, TOutputImage >::GetForegroundValue ( ) const [virtual]

Set get the binary ON value of the input image.

template<typename TInputImage , typename TOutputImage >
virtual unsigned int itk::STAPLEImageFilter< TInputImage, TOutputImage >::GetMaximumIterations ( ) const [virtual]

Set/Get the maximum number of iterations after which the STAPLE algorithm will be considered to have converged. In general this SHOULD NOT be set and the algorithm should be allowed to converge on its own.

template<typename TInputImage , typename TOutputImage >
virtual const char* itk::STAPLEImageFilter< TInputImage, TOutputImage >::GetNameOfClass ( ) const [virtual]

Run-time type information (and related methods)

Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.

template<typename TInputImage , typename TOutputImage >
const std::vector< double >& itk::STAPLEImageFilter< TInputImage, TOutputImage >::GetSensitivity ( ) const [inline]

After the filter is updated, this method returns a std::vector<double> of all Sensitivity (true positive fraction, p) values for the expert input volumes.

Definition at line 173 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
double itk::STAPLEImageFilter< TInputImage, TOutputImage >::GetSensitivity ( unsigned int  i) [inline]

After the filter is updated, this method returns the Sensitivity (true positive fraction, p) value for the i-th expert input volume.

Definition at line 180 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
const std::vector< double >& itk::STAPLEImageFilter< TInputImage, TOutputImage >::GetSpecificity ( ) const [inline]

After the filter is updated, this method returns a std::vector<double> of all Specificity (true negative fraction, q) values for the expert input volumes.

Definition at line 165 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
double itk::STAPLEImageFilter< TInputImage, TOutputImage >::GetSpecificity ( unsigned int  i) [inline]

After the filter is updated, this method returns the Specificity (true negative fraction, q) value for the i-th expert input volume.

Definition at line 192 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
static Pointer itk::STAPLEImageFilter< TInputImage, TOutputImage >::New ( ) [static]

Method for creation through the object factory.

Reimplemented from itk::Object.

template<typename TInputImage , typename TOutputImage >
void itk::STAPLEImageFilter< TInputImage, TOutputImage >::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< TInputImage, TOutputImage >.

template<typename TInputImage , typename TOutputImage >
void itk::STAPLEImageFilter< TInputImage, TOutputImage >::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< TInputImage, TOutputImage >.

template<typename TInputImage , typename TOutputImage >
virtual void itk::STAPLEImageFilter< TInputImage, TOutputImage >::SetConfidenceWeight ( double  _arg) [virtual]

Scales the estimated prior probability that a pixel will be inside the targeted object of segmentation. The default prior probability g_t is calculated automatically as the average fraction of positively classified pixels to the total size of the volume (across all input volumes). ConfidenceWeight will scale this default value as g_t = g_t * ConfidenceWeight. In general, ConfidenceWeight should be left to the default of 1.0.

template<typename TInputImage , typename TOutputImage >
virtual void itk::STAPLEImageFilter< TInputImage, TOutputImage >::SetForegroundValue ( InputPixelType  _arg) [virtual]

Set get the binary ON value of the input image.

template<typename TInputImage , typename TOutputImage >
virtual void itk::STAPLEImageFilter< TInputImage, TOutputImage >::SetMaximumIterations ( unsigned int  _arg) [virtual]

Set/Get the maximum number of iterations after which the STAPLE algorithm will be considered to have converged. In general this SHOULD NOT be set and the algorithm should be allowed to converge on its own.

template<typename TInputImage , typename TOutputImage >
itk::STAPLEImageFilter< TInputImage, TOutputImage >::typedef ( Concept::HasNumericTraits< InputPixelType )

Begin concept checking This class requires InputHasNumericTraitsCheck in the form of ( Concept::HasNumericTraits< InputPixelType > )


Member Data Documentation

template<typename TInputImage , typename TOutputImage >
const unsigned int itk::STAPLEImageFilter< TInputImage, TOutputImage >::ImageDimension = TOutputImage::ImageDimension [static]

Extract some information from the image types. Dimensionality of the two images is assumed to be the same.

Definition at line 146 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
double itk::STAPLEImageFilter< TInputImage, TOutputImage >::m_ConfidenceWeight [private]

Definition at line 252 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
unsigned int itk::STAPLEImageFilter< TInputImage, TOutputImage >::m_ElapsedIterations [private]

Definition at line 249 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
InputPixelType itk::STAPLEImageFilter< TInputImage, TOutputImage >::m_ForegroundValue [private]

Definition at line 248 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
unsigned int itk::STAPLEImageFilter< TInputImage, TOutputImage >::m_MaximumIterations [private]

Definition at line 250 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
std::vector< double > itk::STAPLEImageFilter< TInputImage, TOutputImage >::m_Sensitivity [private]

Definition at line 254 of file itkSTAPLEImageFilter.h.

template<typename TInputImage , typename TOutputImage >
std::vector< double > itk::STAPLEImageFilter< TInputImage, TOutputImage >::m_Specificity [private]

Definition at line 255 of file itkSTAPLEImageFilter.h.


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