 |
ITK
4.0.0
Insight Segmentation and Registration Toolkit
|
|
ITK
4.0.0
Insight Segmentation and Registration Toolkit
|
Implementation of the N4 bias field correction algorithm. More...
#include <itkN4BiasFieldCorrectionImageFilter.h>
Inheritance diagram for itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >: Collaboration diagram for itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >:Implementation of the N4 bias field correction algorithm.
The nonparametric nonuniform intensity normalization (N3) algorithm, as introduced by Sled et al. in 1998 is a method for correcting nonuniformity associated with MR images. The algorithm assumes a simple parametric model (Gaussian) for the bias field and does not require tissue class segmentation. In addition, there are only a couple of parameters to tune with the default values performing quite well. N3 has been publicly available as a set of perl scripts (http://www.bic.mni.mcgill.ca/ServicesSoftwareAdvancedImageProcessingTools/HomePage)
The N4 algorithm, encapsulated with this class, is a variation of the original N3 algorithm with the additional benefits of an improved B-spline fitting routine which allows for multiple resolutions to be used during the correction process. We also modify the iterative update component of algorithm such that the residual bias field is continually updated
Notes for the user: 1. Since much of the image manipulation is done in the log space of the intensities, input images with negative and small values (< 1) can produce poor results. 2. The original authors recommend performing the bias field correction on a downsampled version of the original image. 3. A binary mask or a weighted image can be supplied. If a binary mask is specified, those voxels in the input image which correspond to the voxels in the mask image with a value equal to m_MaskLabel, are used to estimate the bias field. If a confidence image is specified, the input voxels are weighted in the b-spline fitting routine according to the confidence voxel values. 4. The filter returns the corrected image. If the bias field is wanted, one can reconstruct it using the class itkBSplineControlPointImageFilter. See the IJ article and the test file for an example. 5. The 'Z' parameter in Sled's 1998 paper is the square root of the class variable 'm_WienerFilterNoise'.
The basic algorithm iterates between sharpening the intensity histogram of the corrected input image and spatially smoothing those results with a B-spline scalar field estimate of the bias field.
Contributed by Nicholas J. Tustison, James C. Gee in the Insight Journal paper: http://hdl.handle.net/10380/3053
J.G. Sled, A.P. Zijdenbos and A.C. Evans. "A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in Data" IEEE Transactions on Medical Imaging, Vol 17, No 1. Feb 1998.
N.J. Tustison, B.B. Avants, P.A. Cook, Y. Zheng, A. Egan, P.A. Yushkevich, and J.C. Gee. "N4ITK: Improved N3 Bias Correction" IEEE Transactions on Medical Imaging, 29(6):1310-1320, June 2010.
Definition at line 93 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef BSplineFilterType::ArrayType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::ArrayType |
Definition at line 136 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef BSplineFilterType::PointDataImageType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::BiasFieldControlPointLatticeType |
Definition at line 135 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef BSplineScatteredDataPointSetToImageFilter< PointSetType, ScalarImageType> itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::BSplineFilterType |
B-sline filter typedefs
Definition at line 134 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef SmartPointer<const Self> itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::ConstPointer |
Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.
Definition at line 101 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef TInputImage itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::InputImageType |
Some convenient typedefs.
Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.
Definition at line 114 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef TMaskImage itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::MaskImageType |
Definition at line 116 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef MaskImageType::PixelType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::MaskPixelType |
Definition at line 117 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef TOutputImage itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::OutputImageType |
Some convenient typedefs.
Reimplemented from itk::ImageSource< TOutputImage >.
Definition at line 115 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef SmartPointer<Self> itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::Pointer |
Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.
Definition at line 100 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef PointSetType::Pointer itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::PointSetPointer |
B-spline smoothing filter argument typedefs
Definition at line 128 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef PointSet<ScalarType, itkGetStaticConstMacro( ImageDimension )> itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::PointSetType |
B-spline smoothing filter argument typedefs
Definition at line 126 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef PointSetType::PointType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::PointType |
B-spline smoothing filter argument typedefs
Definition at line 129 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef RealImageType::Pointer itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::RealImagePointer |
Definition at line 121 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef Image<RealType, ImageDimension> itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::RealImageType |
Definition at line 120 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef float itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::RealType |
Definition at line 119 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef Image<ScalarType, itkGetStaticConstMacro( ImageDimension )> itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::ScalarImageType |
B-spline smoothing filter argument typedefs
Definition at line 127 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef Vector<RealType, 1> itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::ScalarType |
B-spline smoothing filter argument typedefs
Definition at line 125 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef N4BiasFieldCorrectionImageFilter itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::Self |
Standard class typedefs.
Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.
Definition at line 98 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef ImageToImageFilter<TInputImage, TOutputImage> itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::Superclass |
Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.
Definition at line 99 of file itkN4BiasFieldCorrectionImageFilter.h.
| typedef Array<unsigned int> itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::VariableSizeArrayType |
Definition at line 122 of file itkN4BiasFieldCorrectionImageFilter.h.
| itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::N4BiasFieldCorrectionImageFilter | ( | ) | [protected] |
| itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::~N4BiasFieldCorrectionImageFilter | ( | ) | [inline, protected] |
Definition at line 373 of file itkN4BiasFieldCorrectionImageFilter.h.
| itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::N4BiasFieldCorrectionImageFilter | ( | const Self & | ) | [private] |
| RealType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::CalculateConvergenceMeasurement | ( | const RealImageType * | , |
| const RealImageType * | |||
| ) | const [private] |
Convergence is determined by the coefficient of variation of the difference image between the current bias field estimate and the previous estimate.
| virtual::itk::LightObject::Pointer itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, 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.
| void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, 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.
Reimplemented from itk::ImageSource< TOutputImage >.
| virtual RealType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetBiasFieldFullWidthAtHalfMaximum | ( | ) | const [virtual] |
Get the full width at half maximum parameter characterizing the width of the Gaussian deconvolution. Default = 0.15.
| const RealImageType* itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetConfidenceImage | ( | ) | const [inline] |
Get confidence image function. If a confidence image is specified, estimation of the bias field weights the contribution of each voxel according the value of the corresponding voxel in the confidence image. For example, when estimating the bias field using brain , one can use a soft segmentation of the white matter as the confidence image instead of using a hard segmentation of the white matter as the mask image (as has been done in the literature) as an alternative strategy to estimating the bias field.
Definition at line 193 of file itkN4BiasFieldCorrectionImageFilter.h.
References itk::ProcessObject::GetInput().
| virtual RealType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetConvergenceThreshold | ( | ) | const [virtual] |
Get the convergence threshold. Convergence is determined by the coefficient of variation of the difference image between the current bias field estimate and the previous estimate. If this value is less than the specified threshold, the algorithm proceeds to the next fitting level or terminates if it is at the last level.
| virtual RealType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetCurrentConvergenceMeasurement | ( | ) | const [virtual] |
Get the current convergence measurement. This is a helper function for reporting observations.
| virtual unsigned int itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetCurrentLevel | ( | ) | const [virtual] |
Get the current fitting level. This is a helper function for reporting observations.
| virtual unsigned int itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetElapsedIterations | ( | ) | const [virtual] |
Get the number of elapsed iterations. This is a helper function for reporting observations.
| virtual BiasFieldControlPointLatticeType::Pointer itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetLogBiasFieldControlPointLattice | ( | ) | const [virtual] |
Typically, a reduced size image is used as input to the N4 filter using something like itkShrinkImageFilter. Since the output is a corrected version of the input, the user will probably want to apply the bias field correction to the full resolution image. This can be done by using the LogBiasFieldControlPointLattice to reconstruct the bias field at the full image resolution (using the class BSplineControlPointImageFilter).
| const MaskImageType* itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetMaskImage | ( | ) | const [inline] |
Get mask image function. If a binary mask image is specified, only those input image voxels corresponding with mask image values equal to m_MaskLabel are used in estimating the bias field.
Definition at line 161 of file itkN4BiasFieldCorrectionImageFilter.h.
References itk::ProcessObject::GetInput().
| virtual MaskPixelType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetMaskLabel | ( | ) | const [virtual] |
Get mask label function. If a binary mask image is specified, only those input image voxels corresponding with mask image values equal to m_MaskLabel are used in estimating the bias field. Default = 1.
| virtual VariableSizeArrayType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetMaximumNumberOfIterations | ( | ) | const [virtual] |
Get the maximum number of iterations specified at each fitting level. Default = 50.
| virtual const char* itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetNameOfClass | ( | ) | const [virtual] |
Runtime information support.
Reimplemented from itk::ImageToImageFilter< TInputImage, TOutputImage >.
| virtual ArrayType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetNumberOfControlPoints | ( | ) | const [virtual] |
Get the control point grid size definining the B-spline estimate of the scalar bias field. In each dimension, the B-spline mesh size is equal to the number of control points in that dimension minus the spline order. Default = 4 control points in each dimension for a mesh size of 1 in each dimension.
| virtual ArrayType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetNumberOfFittingLevels | ( | ) | const [virtual] |
Get the number of fitting levels. One of the contributions of N4 is the introduction of a multi-scale approach to fitting. This allows one to specify a B-spline mesh size for initial fitting followed by a doubling of the mesh resolution for each subsequent fitting level. Default = 1 level.
| virtual unsigned int itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetNumberOfHistogramBins | ( | ) | const [virtual] |
Get number of bins defining the log input intensity histogram. Default = 200.
| virtual unsigned int itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetSplineOrder | ( | ) | const [virtual] |
Get the spline order defining the bias field estimate. Default = 3.
| virtual RealType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::GetWienerFilterNoise | ( | ) | const [virtual] |
Get the noise estimate defining the Wiener filter. Default = 0.01.
| static Pointer itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::New | ( | ) | [static] |
Standard New method.
Reimplemented from itk::Object.
| void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, 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 >.
| void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, 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 >.
| virtual void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetBiasFieldFullWidthAtHalfMaximum | ( | RealType | _arg | ) | [virtual] |
Set the full width at half maximum parameter characterizing the width of the Gaussian deconvolution. Default = 0.15.
| void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetConfidenceImage | ( | const RealImageType * | image | ) | [inline] |
Set confidence image function. If a confidence image is specified, estimation of the bias field weights the contribution of each voxel according the value of the corresponding voxel in the confidence image. For example, when estimating the bias field using brain , one can use a soft segmentation of the white matter as the confidence image instead of using a hard segmentation of the white matter as the mask image (as has been done in the literature) as an alternative strategy to estimating the bias field.
Definition at line 176 of file itkN4BiasFieldCorrectionImageFilter.h.
| virtual void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetConvergenceThreshold | ( | RealType | _arg | ) | [virtual] |
Set the convergence threshold. Convergence is determined by the coefficient of variation of the difference image between the current bias field estimate and the previous estimate. If this value is less than the specified threshold, the algorithm proceeds to the next fitting level or terminates if it is at the last level.
| void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetInput1 | ( | const InputImageType * | image | ) | [inline] |
The image expected for input for bias correction.
Definition at line 141 of file itkN4BiasFieldCorrectionImageFilter.h.
| void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetInput2 | ( | const MaskImageType * | mask | ) | [inline] |
Set mask image function. If a binary mask image is specified, only those input image voxels corresponding with mask image values equal to m_MaskLabel are used in estimating the bias field.
Definition at line 153 of file itkN4BiasFieldCorrectionImageFilter.h.
| void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetInput3 | ( | const RealImageType * | image | ) | [inline] |
Set confidence image function. If a confidence image is specified, estimation of the bias field weights the contribution of each voxel according the value of the corresponding voxel in the confidence image. For example, when estimating the bias field using brain , one can use a soft segmentation of the white matter as the confidence image instead of using a hard segmentation of the white matter as the mask image (as has been done in the literature) as an alternative strategy to estimating the bias field.
Definition at line 180 of file itkN4BiasFieldCorrectionImageFilter.h.
| void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetMaskImage | ( | const MaskImageType * | mask | ) | [inline] |
Set mask image function. If a binary mask image is specified, only those input image voxels corresponding with mask image values equal to m_MaskLabel are used in estimating the bias field.
Definition at line 149 of file itkN4BiasFieldCorrectionImageFilter.h.
| virtual void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetMaskLabel | ( | MaskPixelType | _arg | ) | [virtual] |
Set mask label function. If a binary mask image is specified, only those input image voxels corresponding with mask image values equal to m_MaskLabel are used in estimating the bias field. Default = 1.
| virtual void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetMaximumNumberOfIterations | ( | VariableSizeArrayType | _arg | ) | [virtual] |
Set the maximum number of iterations specified at each fitting level. Default = 50.
| virtual void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetNumberOfControlPoints | ( | ArrayType | _arg | ) | [virtual] |
Set the control point grid size definining the B-spline estimate of the scalar bias field. In each dimension, the B-spline mesh size is equal to the number of control points in that dimension minus the spline order. Default = 4 control points in each dimension for a mesh size of 1 in each dimension.
| virtual void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetNumberOfFittingLevels | ( | ArrayType | _arg | ) | [virtual] |
Set the number of fitting levels. One of the contributions of N4 is the introduction of a multi-scale approach to fitting. This allows one to specify a B-spline mesh size for initial fitting followed by a doubling of the mesh resolution for each subsequent fitting level. Default = 1 level.
| void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetNumberOfFittingLevels | ( | unsigned int | n | ) | [inline] |
Set the number of fitting levels. One of the contributions of N4 is the introduction of a multi-scale approach to fitting. This allows one to specify a B-spline mesh size for initial fitting followed by a doubling of the mesh resolution for each subsequent fitting level. Default = 1 level.
Definition at line 295 of file itkN4BiasFieldCorrectionImageFilter.h.
References itk::FixedArray< TValueType, VLength >::Fill().
| virtual void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetNumberOfHistogramBins | ( | unsigned int | _arg | ) | [virtual] |
Set number of bins defining the log input intensity histogram. Default = 200.
| virtual void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetSplineOrder | ( | unsigned int | _arg | ) | [virtual] |
Set the spline order defining the bias field estimate. Default = 3.
| virtual void itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SetWienerFilterNoise | ( | RealType | _arg | ) | [virtual] |
Set the noise estimate defining the Wiener filter. Default = 0.01.
| RealImagePointer itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::SharpenImage | ( | const RealImageType * | ) | const [private] |
Sharpen the intensity histogram of the current estimate of the corrected image and map those results to a new estimate of the unsmoothed corrected image.
| RealImagePointer itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::UpdateBiasFieldEstimate | ( | RealImageType * | ) | [private] |
Given the unsmoothed estimate of the bias field, this function smooths the estimate and adds the resulting control point values to the total bias field estimate.
const unsigned int itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::ImageDimension = TInputImage::ImageDimension [static] |
ImageDimension constants
Definition at line 111 of file itkN4BiasFieldCorrectionImageFilter.h.
RealType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_BiasFieldFullWidthAtHalfMaximum [private] |
Definition at line 419 of file itkN4BiasFieldCorrectionImageFilter.h.
RealType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_ConvergenceThreshold [private] |
Definition at line 425 of file itkN4BiasFieldCorrectionImageFilter.h.
RealType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_CurrentConvergenceMeasurement [private] |
Definition at line 426 of file itkN4BiasFieldCorrectionImageFilter.h.
unsigned int itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_CurrentLevel [private] |
Definition at line 427 of file itkN4BiasFieldCorrectionImageFilter.h.
unsigned int itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_ElapsedIterations [private] |
Definition at line 424 of file itkN4BiasFieldCorrectionImageFilter.h.
BiasFieldControlPointLatticeType::Pointer itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_LogBiasFieldControlPointLattice [private] |
Definition at line 432 of file itkN4BiasFieldCorrectionImageFilter.h.
MaskPixelType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_MaskLabel [private] |
Definition at line 413 of file itkN4BiasFieldCorrectionImageFilter.h.
VariableSizeArrayType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_MaximumNumberOfIterations [private] |
Definition at line 423 of file itkN4BiasFieldCorrectionImageFilter.h.
ArrayType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_NumberOfControlPoints [private] |
Definition at line 435 of file itkN4BiasFieldCorrectionImageFilter.h.
ArrayType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_NumberOfFittingLevels [private] |
Definition at line 436 of file itkN4BiasFieldCorrectionImageFilter.h.
unsigned int itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_NumberOfHistogramBins [private] |
Definition at line 417 of file itkN4BiasFieldCorrectionImageFilter.h.
unsigned int itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_SplineOrder [private] |
Definition at line 434 of file itkN4BiasFieldCorrectionImageFilter.h.
RealType itk::N4BiasFieldCorrectionImageFilter< TInputImage, TMaskImage, TOutputImage >::m_WienerFilterNoise [private] |
Definition at line 418 of file itkN4BiasFieldCorrectionImageFilter.h.
1.7.4
