java.lang.Object
- gov.nih.mipav.model.algorithms.levelset.LseSegmenter
- - gov.nih.mipav.model.algorithms.levelset.LseSegThreshold2

```
public class LseSegThreshold2
extends LseSegmenter
```
A segmenter for 2D images based on the PDE described in LseThreshold2.

Version:

0.1 November 7, 2006

Author:

David Eberly

Field Summary

Fields
Modifier and Type	Field	Description
`private float[]`	`m_afBlurCurvFlow`	The blurred curvature flow image (intermediate image).
`private float[]`	`m_afCurvFlow`	The curvature flow image (intermediate image).
`private float[]`	`m_afFeature`	The feature image.
`private float[]`	`m_afGradMagBlurCurvFlow`	The gradient magnitude of the blurred curvature flow image (intermediate image).
`private int`	`m_eScaleType`	The scaling type of the image (default = LsePdeFilter.NONE).
`private float`	`m_fAlpha`	The default parameters used in the sigmoid function that modifies the gradient magnitude (default alpha = 1, default beta = 0, default min = 0, default max = 1).
`private float`	`m_fBeta`	The default parameters used in the sigmoid function that modifies the gradient magnitude (default alpha = 1, default beta = 0, default min = 0, default max = 1).
`private float`	`m_fBorderValue`	The border value of the image (default = Float.MAX_VALUE).
`private float`	`m_fLower`	Parameters for the threshold segmentation.
`private float`	`m_fMax`	The default parameters used in the sigmoid function that modifies the gradient magnitude (default alpha = 1, default beta = 0, default min = 0, default max = 1).
`private float`	`m_fMin`	The default parameters used in the sigmoid function that modifies the gradient magnitude (default alpha = 1, default beta = 0, default min = 0, default max = 1).
`private float`	`m_fScale`	The scale used for computing the blurred gradient magnitude image (default = 0).
`private float`	`m_fTimeStep`	The time step of the PDE solver (default = 0.001).
`private float`	`m_fUpper`	Parameters for the threshold segmentation.
`private float`	`m_fXSpacing`	The x-spacing of the image.
`private float`	`m_fYSpacing`	The y-spacing of the image.
`private int`	`m_iDIterations`	The number of iterations to use in the nonlinear diffusion used to create the feature image (default = 0).
`private int`	`m_iXBound`	The x-bound of the image.
`private int`	`m_iYBound`	The y-bound of the image.

Fields inherited from class gov.nih.mipav.model.algorithms.levelset.LseSegmenter
m_abMask, m_abSignedDistanceMask, m_afData, m_afSignedDistance, m_aiBoundary, m_aiInterior, m_fCurrentDistance, m_fMaxDistance, m_iQuantity, m_kFastMarcher, m_kLevelSetEvolver

Constructor Summary

Constructors
Constructor	Description
`LseSegThreshold2(int iXBound, int iYBound, float fXSpacing, float fYSpacing, float[] afData, boolean[] abMask)`	Create a threshold-based segmenter.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`beginCoarse(int[] aiSeeds)`	The region-growing fast march.
`boolean`	`beginDistanceTransform(float fMaxDistance)`	The signed-distance-transform fast march.
`void`	`beginEvolution(float fAdvectionWeight, float fPropagationWeight, float fCurvatureWeight, float fLaplacianWeight, float fTimeStep)`	The level-set evolution.
`void`	`createFeatureImage()`	Create the feature image using the parameters set by the four parameter-setting functions defined above.
`(package private) float[]`	`getBlurCurvFlowImage()`	Get the blurred curvature flow image.
`(package private) float[]`	`getCurvFlowImage()`	Get the curvature flow image.
`(package private) float[]`	`getFeatureImage()`	Get the feature image.
`(package private) float[]`	`getGradMagBlurCurvFlowImage()`	Get the gradient magnitude of the blurred curvature flow image.
`void`	`setDiffusionIterations(int iDIterations)`	Set the number of iterations to use in the nonlinear diffusion (curvature flow filter) applied to the input image.
`void`	`setGradientMagnitudeScale(float fScale)`	Set the scale to use in computing the blurred gradient magnitude of the curvature flow image.
`void`	`setPDEParameters(float fBorderValue, int eScaleType, float fTimeStep)`	Parameters for the feature image creation.
`void`	`setSigmoidFilter(float fAlpha, float fBeta, float fMin, float fMax)`	Set the parameters for the sigmoid function through which the blurred gradient magnitude image is processed.
`void`	`setThresholds(float fLower, float fUpper)`	The lower and upper thresholds must be set before calling beginEvolution.

Methods inherited from class gov.nih.mipav.model.algorithms.levelset.LseSegmenter
endCoarse, endDistanceTransform, endEvolution, getBoundary, getFastMarcher, getInterior, getLevelSetEvolver, getSignedDistanceImage, getSignedDistanceMask, iterateCoarse, iterateDistanceTransform, iterateEvolution

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - m_iXBound
```
private int m_iXBound
```
    The x-bound of the image.
  - m_iYBound
```
private int m_iYBound
```
    The y-bound of the image.
  - m_fXSpacing
```
private float m_fXSpacing
```
    The x-spacing of the image.
  - m_fYSpacing
```
private float m_fYSpacing
```
    The y-spacing of the image.
  - m_fBorderValue
```
private float m_fBorderValue
```
    The border value of the image (default = Float.MAX_VALUE).
  - m_eScaleType
```
private int m_eScaleType
```
    The scaling type of the image (default = LsePdeFilter.NONE).
  - m_fTimeStep
```
private float m_fTimeStep
```
    The time step of the PDE solver (default = 0.001).
  - m_iDIterations
```
private int m_iDIterations
```
    The number of iterations to use in the nonlinear diffusion used to create the feature image (default = 0).
  - m_fScale
```
private float m_fScale
```
    The scale used for computing the blurred gradient magnitude image (default = 0).
  - m_fAlpha
```
private float m_fAlpha
```
    The default parameters used in the sigmoid function that modifies the gradient magnitude (default alpha = 1, default beta = 0, default min = 0, default max = 1).
  - m_fBeta
```
private float m_fBeta
```
    The default parameters used in the sigmoid function that modifies the gradient magnitude (default alpha = 1, default beta = 0, default min = 0, default max = 1).
  - m_fMin
```
private float m_fMin
```
    The default parameters used in the sigmoid function that modifies the gradient magnitude (default alpha = 1, default beta = 0, default min = 0, default max = 1).
  - m_fMax
```
private float m_fMax
```
    The default parameters used in the sigmoid function that modifies the gradient magnitude (default alpha = 1, default beta = 0, default min = 0, default max = 1).
  - m_afFeature
```
private float[] m_afFeature
```
    The feature image.
  - m_afCurvFlow
```
private float[] m_afCurvFlow
```
    The curvature flow image (intermediate image).
  - m_afBlurCurvFlow
```
private float[] m_afBlurCurvFlow
```
    The blurred curvature flow image (intermediate image).
  - m_afGradMagBlurCurvFlow
```
private float[] m_afGradMagBlurCurvFlow
```
    The gradient magnitude of the blurred curvature flow image (intermediate image).
  - m_fLower
```
private float m_fLower
```
    Parameters for the threshold segmentation.
  - m_fUpper
```
private float m_fUpper
```
    Parameters for the threshold segmentation.
- Constructor Detail
  - LseSegThreshold2
```
public LseSegThreshold2(int iXBound,
                        int iYBound,
                        float fXSpacing,
                        float fYSpacing,
                        float[] afData,
                        boolean[] abMask)
```
    Create a threshold-based segmenter.
    
    Parameters:
    
    iXBound - The x-bound of the image.
    
    iYBound - The y-bound of the image.
    
    fXSpacing - The x-spacing of the image.
    
    fYSpacing - The y-spacing of the image.
    
    afData - The image elements.
    
    abMask - The image masks that determine which pixels are processed by the segmenter.
- Method Detail
  - setPDEParameters
```
public final void setPDEParameters(float fBorderValue,
                                   int eScaleType,
                                   float fTimeStep)
```
    Parameters for the feature image creation. You may set these anytime before a sequence of fast-march iterations. The PDE filters require you to specify how to handle boundary values. If you want Dirichlet conditions (constant boundary), set BorderValue to a finite number. If you want Neumann conditions (zero-derivative boundary), set the BorderValue to Float.MAX_VALUE. If you want the original image to be scaled, choose the Type parameter appropriately. The input image is processed by a curvature-based anisotropic diffusion filter; the number of iterations is DIterations. The gradient magnitude at the specified Scale is computed for the output of the diffusion filter. The gradient magnitude is transformed to the feature image by a sigmoid function:
    
    feature(x) = min + (max-min)/(1 + exp(-(x-beta)/alpha))
    
    The inverse of the feature image values are the speeds used in the fast marching algorithm.
    
    Specified by:
    
    setPDEParameters in class LseSegmenter
    
    Parameters:
    
    fBorderValue - The border value to use when processing boundary pixels.
    
    eScaleType - The scaling type for the image.
    
    fTimeStep - The time step for the PDE solver.
  - setDiffusionIterations
```
public final void setDiffusionIterations(int iDIterations)
```
    Set the number of iterations to use in the nonlinear diffusion (curvature flow filter) applied to the input image.
    
    Specified by:
    
    setDiffusionIterations in class LseSegmenter
    
    Parameters:
    
    iDIterations - The number of iterations.
  - setGradientMagnitudeScale
```
public final void setGradientMagnitudeScale(float fScale)
```
    Set the scale to use in computing the blurred gradient magnitude of the curvature flow image.
    
    Specified by:
    
    setGradientMagnitudeScale in class LseSegmenter
    
    Parameters:
    
    fScale - The scale for the blurred gradient magnitude image.
  - setSigmoidFilter
```
public final void setSigmoidFilter(float fAlpha,
                                   float fBeta,
                                   float fMin,
                                   float fMax)
```
    Set the parameters for the sigmoid function through which the blurred gradient magnitude image is processed.
    
    Specified by:
    
    setSigmoidFilter in class LseSegmenter
    
    Parameters:
    
    fAlpha - The variance of the function.
    
    fBeta - The center of the function.
    
    fMin - The minimum of the function.
    
    fMax - The maximum of the function.
  - createFeatureImage
```
public final void createFeatureImage()
```
    Create the feature image using the parameters set by the four parameter-setting functions defined above. Since the quality of the segmentation depends on these parameters, createFeatureImage() allows you to experiment with the parameters before you start the segmentation.
  - getFeatureImage
```
float[] getFeatureImage()
```
    Get the feature image. The result is relevant only after a call to computeFeatureImage or beginCoarse.
    
    Returns:
    
    The feature image.
  - getCurvFlowImage
```
float[] getCurvFlowImage()
```
    Get the curvature flow image. The result is relevant only after a call to computeFeatureImage or beginCoarse.
    
    Returns:
    
    The curvature flow image.
  - getBlurCurvFlowImage
```
float[] getBlurCurvFlowImage()
```
    Get the blurred curvature flow image. The result is relevant only after a call to computeFeatureImage or beginCoarse.
    
    Returns:
    
    The blurred curvature flow image.
  - getGradMagBlurCurvFlowImage
```
float[] getGradMagBlurCurvFlowImage()
```
    Get the gradient magnitude of the blurred curvature flow image. The result is relevant only after a call to computeFeatureImage or beginCoarse.
    
    Returns:
    
    The gradient magnitude of the blurred curvature flow image.
  - beginCoarse
```
public void beginCoarse(int[] aiSeeds)
```
    The region-growing fast march. When you call beginCoarse(), the current parameters are used (DIterations, scale, alpha, beta, min, max). You can apply the fast marching algorithm multiple times to the same image, each time starting with beginCoarse();
    
    Overrides:
    
    beginCoarse in class LseSegmenter
    
    Parameters:
    
    aiSeeds - The initial region to grow.
  - beginDistanceTransform
```
public boolean beginDistanceTransform(float fMaxDistance)
```
    The signed-distance-transform fast march. An annulus is computed about the boundary of the coarse-level segmentation. The maximum distance from annulus points to the boundary is the input to this function (in pixel units).
    
    Overrides:
    
    beginDistanceTransform in class LseSegmenter
    
    Parameters:
    
    fMaxDistance - The maximum distance to allow when computing the signed distance transform.
    
    Returns:
    
    'true' iff more iterations are allowed
  - setThresholds
```
public void setThresholds(float fLower,
                          float fUpper)
```
    The lower and upper thresholds must be set before calling beginEvolution.
    
    Parameters:
    
    fLower - The lower threshold for propagation P(x) in the PDE.
    
    fUpper - The upper threshold for propagation P(x) in the PDE.
  - beginEvolution
```
public void beginEvolution(float fAdvectionWeight,
                           float fPropagationWeight,
                           float fCurvatureWeight,
                           float fLaplacianWeight,
                           float fTimeStep)
```
    The level-set evolution.
    
    Overrides:
    
    beginEvolution in class LseSegmenter
    
    Parameters:
    
    fAdvectionWeight - The advection coefficient 'a' in the PDE that controls the evolution.
    
    fPropagationWeight - The propagation coefficient 'b' in the PDE that controls the evolution.
    
    fCurvatureWeight - The curvature coefficient 'c' in the PDE that controls the evolution.
    
    fLaplacianWeight - The Laplacian coefficient 'd' in the PDE that controls the evolution.
    
    fTimeStep - The time step for the PDE solver.

Class LseSegThreshold2

Field Summary

Fields inherited from class gov.nih.mipav.model.algorithms.levelset.LseSegmenter

Constructor Summary

Method Summary

Methods inherited from class gov.nih.mipav.model.algorithms.levelset.LseSegmenter

Methods inherited from class java.lang.Object

Field Detail

m_iXBound

m_iYBound

m_fXSpacing

m_fYSpacing

m_fBorderValue

m_eScaleType

m_fTimeStep

m_iDIterations

m_fScale

m_fAlpha

m_fBeta

m_fMin

m_fMax

m_afFeature

m_afCurvFlow

m_afBlurCurvFlow

m_afGradMagBlurCurvFlow

m_fLower

m_fUpper

Constructor Detail

LseSegThreshold2

Method Detail

setPDEParameters

setDiffusionIterations

setGradientMagnitudeScale

setSigmoidFilter

createFeatureImage

getFeatureImage

getCurvFlowImage

getBlurCurvFlowImage

getGradMagBlurCurvFlowImage

beginCoarse

beginDistanceTransform

setThresholds

beginEvolution