java.lang.Object
- java.lang.Thread
- - gov.nih.mipav.model.algorithms.AlgorithmBase
  - - gov.nih.mipav.model.algorithms.AlgorithmEdgeNMSuppression

All Implemented Interfaces:

AlgorithmInterface, java.awt.event.ActionListener, java.awt.event.WindowListener, java.lang.Runnable, java.util.EventListener
```
public class AlgorithmEdgeNMSuppression
extends AlgorithmBase
implements AlgorithmInterface
```
Calculates the non-maximum suppression of an image at a scale defined by the user. This algorithm produces an edge map of the zero crossings of the non-maximum suppression for 2D images and 2.5D images. Edges are defined as the union of points for which the gradient magnitude assumes a maximum in the gradient direction. Introuduce a local orthonormal coordinate system (u,v) at any point P0, where the v-axis is parallel to the gradient direction at P0, and the u-axis is perpindicular. Iu = sin(a)*Ix - cos(a)*Iy Iv = cos(a)*Ix + sin(a)*Iy cos(a) = Ix/(sqrt(Ix*Ix + Iy*Iy)) evaluated at P0 sin(a) = Iy/(sqrt(Ix*Ix + Iy*Iy)) evaluated at P0 Iu = (Ix*Iy - Ix*Iy)/(sqrt(Ix*Ix + Iy*Iy)) = 0 Iv = (Ix*Ix + Iy*Iy)/(sqrt(Ix*Ix + Iy*Iy)) = sqrt(Ix*Ix + Iy*Iy) Iv*Iv = Ix*Ix + Iy*Iy Ivv = (cos(a)*Ix + sin(a)*Iy)(cos(a)*Ix + sin(a)*Iy) = cos(a)*cos(a)*Ixx + 2*cos(a)*sin(a)*Ixy + sin(a)*sin(a)*Iyy = (Ix*Ix*Ixx + 2*Ix*Iy*Ixy + Iy*Iy*Iyy)/(Ix*Ix + Iy*Iy) Iv*Iv*Ivv = Ix*Ix*Ixx + 2*Ix*Iy*Ixy + Iy*Iy*Iyy Ivvv = (cos(a)*cos(a)*Ixx + 2*cos(a)*sin(a)*Ixy + sin(a)*sin(a)*Iyy)(cos(a)*Ix + sin(a)*Iy) = cos(a)*cos(a)*cos(a)*Ixxx + 3*cos(a)*cos(a)*sin(a)*Ixxy + 3*cos(a)*sin(a)*sin(a)*Ixyy + sin(a)*sin(a)*sin(a)*Iyyy = (Ix*Ix*Ix*Ixxx + 3*Ix*Ix*Iy*Ixxy + 3*Ix*Iy*Iy*Ixyy + Iy*Iy*Iy*Iyyy)/((Ix*Ix + Iy*Iy)**3/2) Iv*Iv*Iv*Ivvv = Ix*Ix*Ix*Ixxx + 3*Ix*Ix*Iy*Ixxy + 3*Ix*Iy*Iy*Ixyy + Iy*Iy*Iy*Iyyy Assuming that the second and third-order directional derivatives of I in the v-direction are not simultaneously zero, a necessary and sufficient condition for P0 to be a gradient maximum in the gradient direction may be stated as: Ivv = 0, Ivvv < 0. Since only the sign information is important, this condition can be restated as: Iv*Iv*Ivv = 0, Iv*Iv*Iv*Ivvv < 0. Reference: Geometry-Driven Diffusion in Computer Vision, Bart M. ter Haar Romeny(Ed.), Chapter2:Linear Scale-Space II: Early Visual Operations by Tony Lindeberg and Bart M. ter Haar Romeny, Kluwer Academic Publishers, 1994, page 45.
For 3D a similar derivation: Iv = cos(a)*sin(b)*Ix + sin(a)*sin(b)*Iy + cos(b)*Iz where spherical coordinates are being used cos(a) = Ix/(sqrt(Ix*Ix + Iy*Iy)) evaluated at P0 sin(a) = Iy/(sqrt(Ix*Ix + Iy*Iy)) evaluated at P0 cos(b) = Iz/(sqrt(Ix*Ix + Iy*Iy + Iz*Iz)) evaluated at P0 sin(b) = sqrt(Ix*Ix + Iy*Iy)/(sqrt(Ix*Ix + Iy*Iy + Iz*Iz)) evaluated at P0 Iv = (Ix*Ix + Iy*Iy + Iz*Iz)/(sqrt(Ix*Ix + Iy*Iy + Iz*Iz)) = sqrt(Ix*Ix + Iy*Iy + Iz*Iz) Iv*Iv = Ix*Ix + Iy*Iy + Iz*Iz Ivv = (cos(a)*sin(b)*Ix + sin(a)*sin(b)*Iy + cos(b)*Iz)(cos(a)*sin(b)*Ix + sin(a)*sin(b)*Iy + cos(b)*Iz) = cos(a)*cos(a)*sin(b)*sin(b)*Ixx + 2*cos(a)*sin(a)*sin(b)*sin(b)*Ixy + 2*cos(a)*cos(b)*sin(b)*Ixz + sin(a)*sin(a)*sin(b)*sin(b)*Iyy + 2*sin(a)*cos(b)*sin(b)*Iyz + cos(b)*cos(b)*Izz = (Ix*Ix*Ixx + 2*Ix*Iy*Ixy + 2*Ix*Iz*Ixz + Iy*Iy*Iyy + 2*Iy*Iz*Iyz + Iz*Iz*Izz)/ (Ix*Ix + Iy*Iy + Iz*Iz) Iv*Iv*Ivv = Ix*Ix*Ixx + 2*Ix*Iy*Ixy + 2*Ix*Iz*Ixz + Iy*Iy*Iyy + 2*Iy*Iz*Iyz + Iz*Iz*Izz Ivvv = (cos(a)*cos(a)*sin(b)*sin(b)*Ixx + 2*cos(a)*sin(a)*sin(b)*sin(b)*Ixy + 2*cos(a)*cos(b)*sin(b)*Ixz + sin(a)*sin(a)*sin(b)*sin(b)*Iyy + 2*sin(a)*cos(b)*sin(b)*Iyz + cos(b)*cos(b)*Izz)(cos(a)*sin(b)*Ix + sin(a)*sin(b)*Iy + cos(b)*Iz) = cos(a)*cos(a)*cos(a)*sin(b)*sin(b)*sin(b)*Ixxx + 3*cos(a)*cos(a)*sin(a)*sin(b)*sin(b)*sin(b)*Ixxy + 3*cos(a)*cos(a)*cos(b)*sin(b)*sin(b)*Ixxz + 3*cos(a)*sin(a)*sin(a)*sin(b)*sin(b)*sin(b)*Ixyy + 6*cos(a)*sin(a)*cos(b)*sin(b)*sin(b)*Ixyz + 3*cos(a)*cos(b)*cos(b)*sin(b)*Ixzz + sin(a)*sin(a)*sin(a)*sin(b)*sin(b)*sin(b)*Iyyy + 3*sin(a)*sin(a)*cos(b)*sin(b)*sin(b)*Iyyz + 3*sin(a)*cos(b)*cos(b)*sin(b)*Iyzz + cos(b)*cos(b)*cos(b)*Izzz = (Ix*Ix*Ix*Ixxx + 3*Ix*Ix*Iy*Ixxy + 3*Ix*Ix*Iz*Ixxz + 3*Ix*Iy*Iy*Ixyy + 6*Ix*Iy*Iz*Ixyz + 3*Ix*Iz*Iz*Ixzz + Iy*Iy*Iy*Iyyy + 3*Iy*Iy*Iz*Iyyz + 3*Iy*Iz*Iz*Iyzz + Iz*Iz*Iz*Izzz)/ ((Ix*Ix + Iy*Iy + Iz*Iz)**1.5) Iv*Iv*Iv*Ivvv = Ix*Ix*Ix*Ixxx + 3*Ix*Ix*Iy*Ixxy + 3*Ix*Ix*Iz*Ixxz + 3*Ix*Iy*Iy*Ixyy + 6*Ix*Iy*Iz*Ixyz + 3*Ix*Iz*Iz*Ixzz + Iy*Iy*Iy*Iyyy + 3*Iy*Iy*Iz*Iyyz + 3*Iy*Iz*Iz*Iyzz + Iz*Iz*Iz*Izzz

Nested Class Summary
- Nested classes/interfaces inherited from class java.lang.Thread
  java.lang.Thread.State, java.lang.Thread.UncaughtExceptionHandler

Field Summary

Fields
Modifier and Type	Field	Description
`private boolean`	`entireImage`	DOCUMENT ME!
`private float[]`	`GxData`	DOCUMENT ME!
`private float[]`	`GxxData`	DOCUMENT ME!
`private float[]`	`GxxxData`	DOCUMENT ME!
`private float[]`	`GxxyData`	DOCUMENT ME!
`private float[]`	`GxxzData`	DOCUMENT ME!
`private float[]`	`GxyData`	DOCUMENT ME!
`private float[]`	`GxyyData`	DOCUMENT ME!
`private float[]`	`GxyzData`	DOCUMENT ME!
`private float[]`	`GxzData`	DOCUMENT ME!
`private float[]`	`GxzzData`	DOCUMENT ME!
`private float[]`	`GyData`	DOCUMENT ME!
`private float[]`	`GyyData`	DOCUMENT ME!
`private float[]`	`GyyyData`	DOCUMENT ME!
`private float[]`	`GyyzData`	DOCUMENT ME!
`private float[]`	`GyzData`	DOCUMENT ME!
`private float[]`	`GyzzData`	DOCUMENT ME!
`private float[]`	`GzData`	DOCUMENT ME!
`private float[]`	`GzzData`	DOCUMENT ME!
`private float[]`	`GzzzData`	DOCUMENT ME!
`private int[]`	`kExtents`	DOCUMENT ME!
`static int`	`MARCHING_SQUARES`	Perform zero crossing detection using the marching squares method.
`static int`	`OLD_DETECTION`	Perform zero crossing detection using Matt's old method.
`private float[]`	`outputBuffer`
`private float[]`	`sigmas`	DOCUMENT ME!
`private int`	`zeroDetectionType`	The type of zero crossing detection to use.
`private ModelImage`	`zXMask`	DOCUMENT ME!

Fields inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase
destFlag, destImage, image25D, mask, maxProgressValue, minProgressValue, multiThreadingEnabled, nthreads, progress, progressModulus, progressStep, runningInSeparateThread, separable, srcImage, threadStopped

Fields inherited from class java.lang.Thread
MAX_PRIORITY, MIN_PRIORITY, NORM_PRIORITY

Constructor Summary

Constructors
Constructor	Description
`AlgorithmEdgeNMSuppression(ModelImage destImg, ModelImage srcImg, float[] sigmas, boolean maskFlag, boolean img25D)`	Creates a new AlgorithmEdgeNMSuppression object.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`algorithmPerformed(AlgorithmBase algorithm)`	Called after an algorithm this listener is registered to exits (maybe successfully, maybe not).
`private void`	`calcStoreInDest2D(int nImages, int detectionType)`	This function produces the EdgeNMSup of input image.
`private void`	`calcStoreInDest3D(int detectionType)`	This function produces the Non-maximum suppression of input image.
`void`	`finalize()`	Prepares this class for destruction.
`static java.util.BitSet`	`genLevelMask(int xDim, int yDim, float[] buffer, float level, int detectionType)`	Generates a zero crossing mask for a 2D function sets a Bitset object to 1 is a zero crossing is detected.
`void`	`genZeroXMask(int slice, float[] buffer, float[] buffer2, int detectionType)`	Generates a zero crossing mask for a 2D function sets a ModelImage to 255 if a zero crossing is detected.
`ModelImage`	`getZeroXMask()`	Accessor to return mask indicating zero crossings.
`private void`	`makeKernels2D()`	Creates Gaussian derivative kernels.
`private void`	`makeKernels3D()`	Creates Gaussian derivative kernels.
`void`	`runAlgorithm()`	Starts the program.

Methods inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase
actionPerformed, addListener, addProgressChangeListener, calculateImageSize, calculatePrincipleAxis, computeElapsedTime, computeElapsedTime, convertIntoFloat, delinkProgressToAlgorithm, delinkProgressToAlgorithmMulti, displayError, errorCleanUp, fireProgressStateChanged, fireProgressStateChanged, fireProgressStateChanged, fireProgressStateChanged, fireProgressStateChanged, generateProgressValues, getDestImage, getElapsedTime, getMask, getMaxProgressValue, getMinProgressValue, getNumberOfThreads, getProgress, getProgressChangeListener, getProgressChangeListeners, getProgressModulus, getProgressStep, getProgressValues, getSrcImage, isCompleted, isImage25D, isMultiThreadingEnabled, isRunningInSeparateThread, isThreadStopped, linkProgressToAlgorithm, linkProgressToAlgorithm, makeProgress, notifyListeners, removeListener, removeProgressChangeListener, run, setCompleted, setImage25D, setMask, setMaxProgressValue, setMinProgressValue, setMultiThreadingEnabled, setNumberOfThreads, setProgress, setProgressModulus, setProgressStep, setProgressValues, setProgressValues, setRunningInSeparateThread, setSrcImage, setStartTime, setThreadStopped, startMethod, windowActivated, windowClosed, windowClosing, windowDeactivated, windowDeiconified, windowIconified, windowOpened

Methods inherited from class java.lang.Thread
activeCount, checkAccess, clone, countStackFrames, currentThread, dumpStack, enumerate, getAllStackTraces, getContextClassLoader, getDefaultUncaughtExceptionHandler, getId, getName, getPriority, getStackTrace, getState, getThreadGroup, getUncaughtExceptionHandler, holdsLock, interrupt, interrupted, isAlive, isDaemon, isInterrupted, join, join, join, onSpinWait, resume, setContextClassLoader, setDaemon, setDefaultUncaughtExceptionHandler, setName, setPriority, setUncaughtExceptionHandler, sleep, sleep, start, stop, suspend, toString, yield

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

- Field Detail
  - MARCHING_SQUARES
```
public static final int MARCHING_SQUARES
```
    Perform zero crossing detection using the marching squares method.
    
    See Also:
    
    Constant Field Values
  - OLD_DETECTION
```
public static final int OLD_DETECTION
```
    Perform zero crossing detection using Matt's old method.
    
    See Also:
    
    Constant Field Values
  - entireImage
```
private boolean entireImage
```
    DOCUMENT ME!
  - GxData
```
private float[] GxData
```
    DOCUMENT ME!
  - GxxData
```
private float[] GxxData
```
    DOCUMENT ME!
  - GxxxData
```
private float[] GxxxData
```
    DOCUMENT ME!
  - GxxyData
```
private float[] GxxyData
```
    DOCUMENT ME!
  - GxxzData
```
private float[] GxxzData
```
    DOCUMENT ME!
  - GxyData
```
private float[] GxyData
```
    DOCUMENT ME!
  - GxyyData
```
private float[] GxyyData
```
    DOCUMENT ME!
  - GxyzData
```
private float[] GxyzData
```
    DOCUMENT ME!
  - GxzData
```
private float[] GxzData
```
    DOCUMENT ME!
  - GxzzData
```
private float[] GxzzData
```
    DOCUMENT ME!
  - GyData
```
private float[] GyData
```
    DOCUMENT ME!
  - GyyData
```
private float[] GyyData
```
    DOCUMENT ME!
  - GyyyData
```
private float[] GyyyData
```
    DOCUMENT ME!
  - GyyzData
```
private float[] GyyzData
```
    DOCUMENT ME!
  - GyzData
```
private float[] GyzData
```
    DOCUMENT ME!
  - GyzzData
```
private float[] GyzzData
```
    DOCUMENT ME!
  - GzData
```
private float[] GzData
```
    DOCUMENT ME!
  - GzzData
```
private float[] GzzData
```
    DOCUMENT ME!
  - GzzzData
```
private float[] GzzzData
```
    DOCUMENT ME!
  - kExtents
```
private int[] kExtents
```
    DOCUMENT ME!
  - sigmas
```
private float[] sigmas
```
    DOCUMENT ME!
  - zeroDetectionType
```
private int zeroDetectionType
```
    The type of zero crossing detection to use.
  - zXMask
```
private ModelImage zXMask
```
    DOCUMENT ME!
  - outputBuffer
```
private float[] outputBuffer
```
- Constructor Detail
  - AlgorithmEdgeNMSuppression
```
public AlgorithmEdgeNMSuppression(ModelImage destImg,
                                  ModelImage srcImg,
                                  float[] sigmas,
                                  boolean maskFlag,
                                  boolean img25D)
```
    Creates a new AlgorithmEdgeNMSuppression object.
    
    Parameters:
    
    destImg - image model where result image is to stored
    
    srcImg - source image model
    
    sigmas - Gaussian's standard deviations in the each dimension
    
    maskFlag - Flag that indicates that the EdgeNMSup will be calculated for the whole image if equal to true
    
    img25D - Flag, if true, indicates that each slice of the 3D volume should be processed independently. 2D images disregard this flag.
- Method Detail
  - genLevelMask
```
public static java.util.BitSet genLevelMask(int xDim,
                                            int yDim,
                                            float[] buffer,
                                            float level,
                                            int detectionType)
```
    Generates a zero crossing mask for a 2D function sets a Bitset object to 1 is a zero crossing is detected.
    
    Parameters:
    
    xDim - the buffer's x dimension
    
    yDim - the buffer's y dimension
    
    buffer - array in which to find zero crossing
    
    level - the level to find the crossing of (usually will be 0)
    
    detectionType - the type of zero crossing method to use
    
    Returns:
    
    DOCUMENT ME!
  - finalize
```
public void finalize()
```
    Prepares this class for destruction.
    
    Overrides:
    
    finalize in class AlgorithmBase
  - genZeroXMask
```
public void genZeroXMask(int slice,
                         float[] buffer,
                         float[] buffer2,
                         int detectionType)
```
    Generates a zero crossing mask for a 2D function sets a ModelImage to 255 if a zero crossing is detected.
    
    Parameters:
    
    slice - DOCUMENT ME!
    
    buffer - array in which to find zero crossing
    
    buffer2 - array which ensures that zero crossing is only counted if buffer2 value at that position is less than zero
    
    detectionType - the type of zero crossing detection to perform
  - getZeroXMask
```
public ModelImage getZeroXMask()
```
    Accessor to return mask indicating zero crossings.
    
    Returns:
    
    ModelImage of zero crossings (2D function); 255 = indicates zero crossing
  - runAlgorithm
```
public void runAlgorithm()
```
    Starts the program.
    
    Specified by:
    
    runAlgorithm in class AlgorithmBase
  - calcStoreInDest2D
```
private void calcStoreInDest2D(int nImages,
                               int detectionType)
```
    This function produces the EdgeNMSup of input image.
    
    Parameters:
    
    nImages - number of images to be blurred. If 2D image then nImage = 1, if 3D image where each image is to processed independently then nImages equals the number of images in the volume.
    
    detectionType - the type of zero crossing detection to perform
  - calcStoreInDest3D
```
private void calcStoreInDest3D(int detectionType)
```
    This function produces the Non-maximum suppression of input image.
    
    Parameters:
    
    detectionType - the type of zero crossing detection to perform
  - makeKernels2D
```
private void makeKernels2D()
```
    Creates Gaussian derivative kernels.
  - makeKernels3D
```
private void makeKernels3D()
```
    Creates Gaussian derivative kernels.
  - algorithmPerformed
```
public void algorithmPerformed(AlgorithmBase algorithm)
```
    Description copied from interface: AlgorithmInterface
    
    Called after an algorithm this listener is registered to exits (maybe successfully, maybe not). If the algorithm is run in a separate thread, this call will be made within that thread. If not, this call will be made from that same, shared thread.
    
    Specified by:
    
    algorithmPerformed in interface AlgorithmInterface
    
    Parameters:
    
    algorithm - the algorithm which has just completed

Class AlgorithmEdgeNMSuppression

Nested Class Summary

Nested classes/interfaces inherited from class java.lang.Thread

Field Summary

Fields inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase

Fields inherited from class java.lang.Thread

Constructor Summary

Method Summary

Methods inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase

Methods inherited from class java.lang.Thread

Methods inherited from class java.lang.Object

Field Detail

MARCHING_SQUARES

OLD_DETECTION

entireImage

GxData

GxxData

GxxxData

GxxyData

GxxzData

GxyData

GxyyData

GxyzData

GxzData

GxzzData

GyData

GyyData

GyyyData

GyyzData

GyzData

GyzzData

GzData

GzzData

GzzzData

kExtents

sigmas

zeroDetectionType

zXMask

outputBuffer

Constructor Detail

AlgorithmEdgeNMSuppression

Method Detail

genLevelMask

finalize

genZeroXMask

getZeroXMask

runAlgorithm

calcStoreInDest2D

calcStoreInDest3D

makeKernels2D

makeKernels3D

algorithmPerformed