Package gov.nih.mipav.model.algorithms

Class AlgorithmEdgeLaplacianSep

java.lang.Object

java.lang.Thread

gov.nih.mipav.model.algorithms.AlgorithmBase

gov.nih.mipav.model.algorithms.AlgorithmEdgeLaplacianSep

All Implemented Interfaces:

ActionListener, WindowListener, Runnable, EventListener

public class AlgorithmEdgeLaplacianSep
extends AlgorithmBase

Calculates the EdgeLap of an image at a scale defined by the user. This algorithm produces an edge map of the zero crossings of the laplacian of the gaussian for 2D images and 2.5D images. This version uses the separable kernel convolution algorithm (faster, but uses more memory). If clearInflectionPoints is true, remove zero crossings which do not correspond to actual edges but to inflection points instead. If a zero crossing is not a point of inflection, then in the direction perpendicular to the crossing edge the sign of the simple central difference operator of the Gaussian of the image times the sign of the third-degree central difference operator of the Gaussian of the image must be less than zero. Reference: "Refining Edges Detected by a LoG Operator" by Fatih Ulupinar and Gerard Medioni, Computer Vision, Graphics, and Image Processing, Vol. 51, 1990, pp. 275-298.

Note: From Design of FIR bilevel Laplacian-of-Gaussian filter by Soo-Chang Pei and Ji-Hwei Horng: "Wiejak showed that the LoG filter may be decomposed into the sum of two separable filters: LoG(X,Y) = -G"(x)*G(y) - G(x)*G"(y) where G and G" are the 1D Gaussian and the second derivative of the 1D Gaussian." The reference is J.S. Wiejak, H. Buxton, and B. F. Buxton, "Convolution with separable masks for early image processing", Computer Vision, Graphics, and Image Processing, 32, 1985, pp. 279-290. Results almost exactly match those of AlgorithmEdgeLaplacian.

Does not work with color images (neither does AlgorithmEdgeLaplacian).

Version:

0.1 Aug 4, 2003

Author:

Evan McCreedy

See Also:

• AlgorithmEdgeLaplacian

Nested Class Summary

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

Thread.Builder, Thread.State, Thread.UncaughtExceptionHandler

Field Summary

Fields

Modifier and Type	Field	Description
private int	clearedPoints
private boolean	clearInflectionPoints
private boolean	entireImage	Flag, if true, indicates that the whole image should be processed.
private float[]	gaussBuffer
private float[]	GxxData	Storage location of the second derivative of the Gaussian in the X direction.
private float[]	GyyData	Storage location of the second derivative of the Gaussian in the Y direction.
private float[]	GzzData	Storage location of the second derivative of the Gaussian in the Z direction.
private int[]	kExtents	Dimensionality of the kernel.
static final int	MARCHING_SQUARES	Perform zero crossing detection using the marching squares method.
static final int	NEGATIVE_EDGES	Mark negative areas of the laplacian image as edges (makes thicker edges - used in BSE).
static final int	OLD_DETECTION	Perform zero crossing detection using Matt's old method.
private float[]	sigmas	Standard deviations of the gaussian used to calculate the kernels.
private int	zeroDetectionType	The type of zero crossing detection to use.
private ModelImage	zXMask	Mask(unsigned byte) of the zero-crossings of the Laplacian of the gaussian.

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
AlgorithmEdgeLaplacianSep(ModelImage destImg, ModelImage srcImg, float[] sigmas, boolean maskFlag, boolean img25D, boolean clearInflectionPoints)	Creates a new AlgorithmEdgeLaplacianSep object.

Method Summary

Modifier and Type	Method	Description
private void	calcStoreInDest2D(int nImages, int detectionType)	This function produces the EdgeLap of input image.
private void	calcStoreInDest3D(int detectionType)	This function produces the EdgeLap of input image.
BitSet	calcZeroXMaskBitset(float[] buffer, int[] extents)	Calculates the zero crossing mask of a 2D image and returns it as a BitSet buffer.
void	finalize()	Prepares this class for destruction.
static 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 where a zero crossing is detected.
void	genZeroXMask(int slice, float[] buffer, int detectionType)	Generates a zero crossing mask for a 2D function.
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.
void	setZeroDetectionType(int type)	Changes the type of zero crossing detection method used.

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, isVirtual, join, join, join, join, ofPlatform, ofVirtual, onSpinWait, resume, setContextClassLoader, setDaemon, setDefaultUncaughtExceptionHandler, setName, setPriority, setUncaughtExceptionHandler, sleep, sleep, sleep, start, startVirtualThread, stop, suspend, threadId, toString, yield

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Details

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

NEGATIVE_EDGES

public static final int NEGATIVE_EDGES

Mark negative areas of the laplacian image as edges (makes thicker edges - used in BSE).

See Also:

• Constant Field Values

entireImage

private boolean entireImage

Flag, if true, indicates that the whole image should be processed. If false only process the image over the mask areas.

GxxData

private float[] GxxData

Storage location of the second derivative of the Gaussian in the X direction.

GyyData

private float[] GyyData

Storage location of the second derivative of the Gaussian in the Y direction.

GzzData

private float[] GzzData

Storage location of the second derivative of the Gaussian in the Z direction.

kExtents

private int[] kExtents

Dimensionality of the kernel.

sigmas

private float[] sigmas

Standard deviations of the gaussian used to calculate the kernels.

zeroDetectionType

private int zeroDetectionType

The type of zero crossing detection to use.

zXMask

private ModelImage zXMask

Mask(unsigned byte) of the zero-crossings of the Laplacian of the gaussian. Non-zero value indicates edge. Zero in the mask image is background.

clearInflectionPoints

private boolean clearInflectionPoints

clearedPoints

private int clearedPoints

gaussBuffer

private float[] gaussBuffer

Constructor Details

AlgorithmEdgeLaplacianSep

public AlgorithmEdgeLaplacianSep(ModelImage destImg,
 ModelImage srcImg,
 float[] sigmas,
 boolean maskFlag,
 boolean img25D,
 boolean clearInflectionPoints)

Creates a new AlgorithmEdgeLaplacianSep 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 EdgeLap 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.

clearInflectionPoints - If true, do not set zero crossings corresponding to points of inflection

Method Details

genLevelMask

public static 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 where a zero crossing is detected.

Parameters:

xDim - x dimension of image

yDim - y dimension of image

buffer - array in which to find zero crossing

level - level to generate zero crossings at

detectionType - the type of zero crossing method to use

Returns:

Bitset representing zero crossings

calcZeroXMaskBitset

public BitSet calcZeroXMaskBitset(float[] buffer,
 int[] extents)

Calculates the zero crossing mask of a 2D image and returns it as a BitSet buffer.

Parameters:

buffer - the slice to work with

extents - the extents of the image

Returns:

BitSet buffer with mask set when a zero crossing is detected

finalize

public void finalize()

Prepares this class for destruction.

Overrides:

finalize in class AlgorithmBase

genZeroXMask

public void genZeroXMask(int slice,
 float[] buffer,
 int detectionType)

Generates a zero crossing mask for a 2D function. Sets a ModelImage to 255 if a zero crossing is detected.

Parameters:

slice - the slice of the volume which we are working on (0 if from 2D image)

buffer - array in which to find zero crossing

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 1 = indicates zero crossing

runAlgorithm

public void runAlgorithm()

Starts the program.

Specified by:

runAlgorithm in class AlgorithmBase

setZeroDetectionType

public void setZeroDetectionType(int type)

Changes the type of zero crossing detection method used.

Parameters:

type - the zero crossing method to use

calcStoreInDest2D

private void calcStoreInDest2D(int nImages,
 int detectionType)

This function produces the EdgeLap of input image.

Parameters:

nImages - number of images on which to find zero crossings. 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 EdgeLap 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.