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static bool | GetGlobalDefaultDebug () |
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static void | GlobalDefaultDebugOff () |
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static void | GlobalDefaultDebugOn () |
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static void | SetGlobalDefaultDebug (bool debugFlag) |
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static void | GlobalWarningDisplayOn () |
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static void | GlobalWarningDisplayOff () |
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static void | SetGlobalWarningDisplay (bool flag) |
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static bool | GetGlobalWarningDisplay () |
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static void | SetGlobalDefaultNumberOfThreads (unsigned int n) |
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static unsigned int | GetGlobalDefaultNumberOfThreads () |
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static double | GetGlobalDefaultCoordinateTolerance () |
| Access the global tolerance to determine congruent spaces. More...
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static void | SetGlobalDefaultCoordinateTolerance (double) |
| Access the global tolerance to determine congruent spaces. More...
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static double | GetGlobalDefaultDirectionTolerance () |
| Access the global tolerance to determine congruent spaces. More...
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static void | SetGlobalDefaultDirectionTolerance (double) |
| Access the global tolerance to determine congruent spaces. More...
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virtual unsigned long | AddITKObserver (const itk::EventObject &, itk::Command *) |
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virtual itk::ProcessObject * | GetActiveProcess () |
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virtual void | OnActiveProcessDelete () |
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virtual void | onCommandDelete (const itk::simple::Command *cmd) SITK_NOEXCEPT |
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virtual void | PreUpdate (itk::ProcessObject *p) |
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virtual void | RemoveITKObserver (EventCommand &e) |
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| NonCopyable () |
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static void | FixNonZeroIndex (TImageType *img) |
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template<class TImageType > |
static TImageType::ConstPointer | CastImageToITK (const Image &img) |
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template<class TImageType > |
static Image | CastITKToImage (TImageType *img) |
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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) |
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static const itk::EventObject & | GetITKEventObject (EventEnum e) |
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template<typename T > |
static std::ostream & | ToStringHelper (std::ostream &os, const T &v) |
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static std::ostream & | ToStringHelper (std::ostream &os, const char &v) |
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static std::ostream & | ToStringHelper (std::ostream &os, const signed char &v) |
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static std::ostream & | ToStringHelper (std::ostream &os, const unsigned char &v) |
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Segments structures in images based on a user supplied edge potential map.
- IMPORTANT
- The SegmentationLevelSetImageFilter class and the ShapeDetectionLevelSetFunction class contain additional information necessary to gain full understanding of how to use this filter.
- OVERVIEW
- This class is a level set method segmentation filter. An initial contour is propagated outwards (or inwards) until it ''sticks'' to the shape boundaries. This is done by using a level set speed function based on a user supplied edge potential map. This approach for segmentation follows that of Malladi et al (1995).
- INPUTS
- This filter requires two inputs. The first input is a initial level set. The initial level set is a real image which contains the initial contour/surface as the zero level set. For example, a signed distance function from the initial contour/surface is typically used. Note that for this algorithm the initial contour has to be wholly within (or wholly outside) the structure to be segmented.
- The second input is the feature image. For this filter, this is the edge potential map. General characteristics of an edge potential map is that it has values close to zero in regions near the edges and values close to one inside the shape itself. Typically, the edge potential map is compute from the image gradient, for example:
where is image intensity and is the derivative of Gaussian operator.
- See SegmentationLevelSetImageFilter and SparseFieldLevelSetImageFilter for more information on Inputs.
- PARAMETERS
- The PropagationScaling parameter can be used to switch from propagation outwards (POSITIVE scaling parameter) versus propagating inwards (NEGATIVE scaling parameter).
The smoothness of the resulting contour/surface can be adjusted using a combination of PropagationScaling and CurvatureScaling parameters. The larger the CurvatureScaling parameter, the smoother the resulting contour. The CurvatureScaling parameter should be non-negative for proper operation of this algorithm. To follow the implementation in Malladi et al paper, set the PropagtionScaling to and CurvatureScaling to .
Note that there is no advection term for this filter. Setting the advection scaling will have no effect.
- OUTPUTS
- The filter outputs a single, scalar, real-valued image. Negative values in the output image represent the inside of the segmentated region and positive values in the image represent the outside of the segmented region. The zero crossings of the image correspond to the position of the propagating front.
- See SparseFieldLevelSetImageFilter and SegmentationLevelSetImageFilter for more information.
- REFERENCES
- "Shape Modeling with Front Propagation: A Level Set Approach", R. Malladi, J. A. Sethian and B. C. Vermuri. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol 17, No. 2, pp 158-174, February 1995
- See also
- SegmentationLevelSetImageFilter
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ShapeDetectionLevelSetFunction
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SparseFieldLevelSetImageFilter
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itk::simple::ShapeDetectionLevelSet for the procedural interface
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itk::ShapeDetectionLevelSetImageFilter for the Doxygen on the original ITK class.
Definition at line 84 of file sitkShapeDetectionLevelSetImageFilter.h.