ITK
4.2.0
Insight Segmentation and Registration Toolkit
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#include <itkCumulativeGaussianOptimizer.h>
Public Types | |
typedef SmartPointer< const Self > | ConstPointer |
typedef CumulativeGaussianCostFunction | CostFunctionType |
typedef CostFunctionType::MeasureType | MeasureType |
typedef SmartPointer< Self > | Pointer |
typedef CumulativeGaussianOptimizer | Self |
typedef MultipleValuedNonLinearOptimizer | Superclass |
Public Types inherited from itk::MultipleValuedNonLinearOptimizer | |
typedef CostFunctionType::Pointer | CostFunctionPointer |
typedef Array2D< double > | DerivativeType |
typedef Superclass::ParametersType | ParametersType |
Public Types inherited from itk::NonLinearOptimizer | |
typedef Superclass::ScalesType | ScalesType |
Public Types inherited from itk::Optimizer | |
Public Types inherited from itk::Object | |
Public Types inherited from itk::LightObject |
Public Member Functions | |
virtual ::itk::LightObject::Pointer | CreateAnother (void) const |
virtual const char * | GetNameOfClass () const |
const std::string | GetStopConditionDescription () const |
void | PrintArray (MeasureType *array) |
void | SetDataArray (MeasureType *dataArray) |
void | StartOptimization () |
virtual void | SetDifferenceTolerance (double _arg) |
virtual void | SetVerbose (bool _arg) |
virtual double | GetComputedMean () |
virtual double | GetComputedStandardDeviation () |
virtual double | GetUpperAsymptote () |
virtual double | GetLowerAsymptote () |
virtual MeasureType * | GetFinalSampledArray () |
virtual double | GetFitError () |
Public Member Functions inherited from itk::MultipleValuedNonLinearOptimizer | |
virtual void | SetCostFunction (CostFunctionType *costFunction) |
Static Public Member Functions | |
static Pointer | New () |
Protected Member Functions | |
CumulativeGaussianOptimizer () | |
void | PrintSelf (std::ostream &os, Indent indent) const |
virtual | ~CumulativeGaussianOptimizer () |
Protected Member Functions inherited from itk::MultipleValuedNonLinearOptimizer | |
MultipleValuedNonLinearOptimizer () | |
virtual | ~MultipleValuedNonLinearOptimizer () |
NonLinearOptimizer () | |
virtual | ~NonLinearOptimizer () |
Protected Member Functions inherited from itk::Optimizer | |
Optimizer () | |
virtual void | SetCurrentPosition (const ParametersType ¶m) |
virtual | ~Optimizer () |
Protected Member Functions inherited from itk::Object | |
Object () | |
bool | PrintObservers (std::ostream &os, Indent indent) const |
virtual void | SetTimeStamp (const TimeStamp &time) |
virtual | ~Object () |
Protected Member Functions inherited from itk::LightObject | |
virtual LightObject::Pointer | InternalClone () const |
LightObject () | |
virtual void | PrintHeader (std::ostream &os, Indent indent) const |
virtual void | PrintTrailer (std::ostream &os, Indent indent) const |
virtual | ~LightObject () |
Private Member Functions | |
MeasureType * | ExtendGaussian (MeasureType *originalArray, MeasureType *extendedArray, int startingPointForInsertion) |
double | FindAverageSumOfSquaredDifferences (MeasureType *array1, MeasureType *array2) |
void | FindParametersOfGaussian (MeasureType *sampledGaussianArray) |
void | MeasureGaussianParameters (MeasureType *array) |
void | PrintComputedParameterHeader () |
void | PrintComputedParameters () |
MeasureType * | RecalculateExtendedArrayFromGaussianParameters (MeasureType *originalArray, MeasureType *extendedArray, int startingPointForInsertion) |
double | VerticalBestShift (MeasureType *originalArray, MeasureType *newArray) |
Private Attributes | |
double | m_ComputedAmplitude |
double | m_ComputedMean |
double | m_ComputedStandardDeviation |
double | m_ComputedTransitionHeight |
MeasureType * | m_CumulativeGaussianArray |
double | m_DifferenceTolerance |
MeasureType * | m_FinalSampledArray |
double | m_FitError |
double | m_LowerAsymptote |
double | m_OffsetForMean |
std::ostringstream | m_StopConditionDescription |
double | m_UpperAsymptote |
bool | m_Verbose |
Additional Inherited Members | |
Protected Attributes inherited from itk::MultipleValuedNonLinearOptimizer | |
CostFunctionPointer | m_CostFunction |
This is an optimizer specific to estimating the parameters of Cumulative Gaussian sampled data.
This optimizer will only work if the data array is sampled from a Cumulative Gaussian curve. It's more of a curve fitter than an optimizer, with the advantage of being fast and specific. It works by taking the derivative of the Cumulative Gaussian sample then repeatedly extending the tails of the Gaussian and recalculating the Gaussian parameters until the change in iterations is within tolerance or very small. The Gaussian is then integrated to reproduce the Cumulative Gaussian and the asymptotes are estimated by using least squares fit to estimate the constant from integration.
Definition at line 47 of file itkCumulativeGaussianOptimizer.h.
typedef SmartPointer< const Self > itk::CumulativeGaussianOptimizer::ConstPointer |
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Definition at line 56 of file itkCumulativeGaussianOptimizer.h.
Cost function typedef. NOTE: This optimizer is specific to fitting a Cumulative Gaussian.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Definition at line 60 of file itkCumulativeGaussianOptimizer.h.
Data array typedef.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Definition at line 63 of file itkCumulativeGaussianOptimizer.h.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Definition at line 55 of file itkCumulativeGaussianOptimizer.h.
Standard typedefs.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Definition at line 53 of file itkCumulativeGaussianOptimizer.h.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Definition at line 54 of file itkCumulativeGaussianOptimizer.h.
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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::MultipleValuedNonLinearOptimizer.
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Extend the tails of the Gaussian.
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Calculates the squared difference error between each Gaussian iteration loop.
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Given an array sampled from a Gaussin, compute the final parameters.
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Set and get macros.
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Set and get macros.
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Set and get macros.
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Set and get macros.
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Set and get macros.
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Run-time type information (and related methods).
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
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Report the reason for stopping.
Reimplemented from itk::Optimizer.
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Set and get macros.
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Measure the parameters of a Gaussian sampled array.
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Method for creation through the object factory.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
void itk::CumulativeGaussianOptimizer::PrintArray | ( | MeasureType * | array | ) |
Print an array.
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Print the header for output table.
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Print the computed parameters.
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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::MultipleValuedNonLinearOptimizer.
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Recalulate the parameters of the extended Gaussian array.
void itk::CumulativeGaussianOptimizer::SetDataArray | ( | MeasureType * | dataArray | ) |
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Set and get macros.
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Set and get macros.
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Start the optimizer.
Reimplemented from itk::Optimizer.
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Find the constant of the integrated sample.
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The final amplitude of the Gaussian.
Definition at line 110 of file itkCumulativeGaussianOptimizer.h.
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The final mean of the Cumulative Gaussian.
Definition at line 104 of file itkCumulativeGaussianOptimizer.h.
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The final standard deviation of the Cumulative Gaussian.
Definition at line 107 of file itkCumulativeGaussianOptimizer.h.
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The transition height (distance between upper and lower asymptotes) of the Cumulative Gaussian.
Definition at line 114 of file itkCumulativeGaussianOptimizer.h.
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Original data array.
Definition at line 136 of file itkCumulativeGaussianOptimizer.h.
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When to stop the iteration for the Gaussian extension loop.
Definition at line 101 of file itkCumulativeGaussianOptimizer.h.
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Array of values computed from the final parameters of the Cumulative Gaussian.
Definition at line 133 of file itkCumulativeGaussianOptimizer.h.
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Least squares fit error as a measure of goodness.
Definition at line 129 of file itkCumulativeGaussianOptimizer.h.
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The final lower asymptote of the Cumulative Gaussian.
Definition at line 120 of file itkCumulativeGaussianOptimizer.h.
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Offset for the mean calculation.
Definition at line 123 of file itkCumulativeGaussianOptimizer.h.
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Describe the stop condition
Definition at line 166 of file itkCumulativeGaussianOptimizer.h.
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The final upper asymptote of the Cumulative Gaussian.
Definition at line 117 of file itkCumulativeGaussianOptimizer.h.
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Flag to print iteration results.
Definition at line 126 of file itkCumulativeGaussianOptimizer.h.