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itkSTAPLEImageFilter.h
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itkSTAPLEImageFilter.h
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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkSTAPLEImageFilter.h
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) 2002 Insight Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkSTAPLEImageFilter_h
#define __itkSTAPLEImageFilter_h
#include "itkImageToImageFilter.h"
#include "itkImage.h"
#include <vector>
namespace itk
{
/** \class STAPLEImageFilter
*
* \brief 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
*
* \par 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.
*
* \par 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.
*
* \par 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.
*
* \par 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.
*
* \par 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. */
template <typename TInputImage, typename TOutputImage>
class ITK_EXPORT STAPLEImageFilter :
public ImageToImageFilter< TInputImage, TOutputImage >
{
public:
/** Standard class typedefs. */
typedef STAPLEImageFilter Self;
typedef ImageToImageFilter< TInputImage, TOutputImage > Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Run-time type information (and related methods) */
itkTypeMacro(STAPLEImageFilter, ImageToImageFilter);
/** Extract some information from the image types. Dimensionality
* of the two images is assumed to be the same. */
typedef typename TOutputImage::PixelType OutputPixelType;
typedef typename TInputImage::PixelType InputPixelType;
typedef typename NumericTraits<InputPixelType>::RealType RealType;
/** Extract some information from the image types. Dimensionality
* of the two images is assumed to be the same. */
itkStaticConstMacro(ImageDimension, unsigned int,
TOutputImage::ImageDimension);
/** Image typedef support */
typedef TInputImage InputImageType;
typedef TOutputImage OutputImageType;
typedef typename InputImageType::Pointer InputImagePointer;
typedef typename OutputImageType::Pointer OutputImagePointer;
/** Superclass typedefs. */
typedef typename Superclass::OutputImageRegionType OutputImageRegionType;
/** Set get the binary ON value of the input image. */
itkSetMacro(ForegroundValue, InputPixelType);
itkGetMacro(ForegroundValue, InputPixelType);
/** 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.*/
const std::vector<double> &GetSpecificity() const
{
return m_Specificity;
}
/** 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. */
const std::vector<double> &GetSensitivity() const
{
return m_Sensitivity;
}
/** After the filter is updated, this method returns the Sensitivity (true
* positive fraction, p) value for the i-th expert input volume. */
double GetSensitivity(unsigned int i)
{
if (i > this->GetNumberOfInputs())
{
itkExceptionMacro(<< "Array reference out of bounds.");
}
return m_Sensitivity[i];
}
/** After the filter is updated, this method returns the Specificity (true
* negative fraction, q) value for the i-th expert input volume. */
double GetSpecificity(unsigned int i)
{
if (i > this->GetNumberOfInputs())
{
itkExceptionMacro(<< "Array reference out of bounds.");
}
return m_Specificity[i];
}
/** 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. */
itkSetMacro(MaximumIterations, unsigned int);
itkGetMacro(MaximumIterations, unsigned int);
/** 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.*/
itkSetMacro(ConfidenceWeight, double);
itkGetMacro(ConfidenceWeight, double);
/** Get the number of elapsed iterations of the iterative E-M algorithm. */
itkGetMacro(ElapsedIterations, unsigned int);
#ifdef ITK_USE_CONCEPT_CHECKING
/** Begin concept checking */
itkConceptMacro(InputHasNumericTraitsCheck,
(Concept::HasNumericTraits<InputPixelType>));
/** End concept checking */
#endif
protected:
STAPLEImageFilter()
{
m_ForegroundValue = NumericTraits<InputPixelType>::One;
m_MaximumIterations = NumericTraits<unsigned int>::max();
m_ElapsedIterations = 0;
m_ConfidenceWeight = 1.0;
}
virtual ~STAPLEImageFilter() {}
void GenerateData( );
void PrintSelf(std::ostream&, Indent) const;
private:
STAPLEImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
InputPixelType m_ForegroundValue;
unsigned int m_ElapsedIterations;
unsigned int m_MaximumIterations;
double m_ConfidenceWeight;
std::vector<double> m_Sensitivity;
std::vector<double> m_Specificity;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkSTAPLEImageFilter.txx"
#endif
#endif