Package gov.nih.mipav.model.algorithms

Class AlgorithmIndependentComponents

java.lang.Object

java.lang.Thread

gov.nih.mipav.model.algorithms.AlgorithmBase

gov.nih.mipav.model.algorithms.AlgorithmIndependentComponents

All Implemented Interfaces:

ActionListener, WindowListener, Runnable, EventListener

public class AlgorithmIndependentComponents
extends AlgorithmBase

This algorithm operates on 2D and 3D black and white and color images. Requirements: 1.) The independent components must be statistically independent. 2.) The independent components must have all nongaussian distributions or one gaussian distribution and all the other distributions nongaussian or the independent components have time dependencies. 3.) For simplicity, assume the unknown mixture matrix is square. In other words, the number of mixtures equals the number of independent components. If the number of independent components is smaller than the number of mixtures, use principal component analysis to reduce the number of mixtures to the number of principal components. Reducing the number of dimensions can reduce noise and prevent overlearning. Filtering the data in the Fourier domain is allowed before performing independent component analysis since applying the Fourier transform to the data does not change the mixing matrix. References: 1.) Independent Component Analysis by Aapo Hyvarinen, Juha Karhunen, and Erkki Oja, John-Wiley invalid input: '&' Sons, Inc., 2001. 2.) Independent Component Analysis A Tutorial Introduction by James V. Stone, Massachusetts Institute of Technology, 2004. 3.) Independent Component Analysis Principles and Practice Edited by Stephen Roberts and Richard Everson, Cambridge University Press, 2001. From the text: "Another very useful thing to do is to reduce the dimension of the data by principal component analysis. This reduces noise and prevents overlearning. It may also solve the problems with data that has a smaller number of independent components than mixtures." Software was tested with images obtained from the site at http://www.lx.it.pt/~lbalmeida/ica/seethrough/index.html, "See-through" data sets for nonlinear source separation by Luis B. Almeida. In the paper "Separating a Real-Life Nonlinear Image Mixture" by Luis B. Almeida, the following average linear and nonlinear separation SNR results are given in decibels for sets 4 thru 8: Set number linear source 1 linear source 2 nonlinear source1 nonlinear source2 4 9.0 8.7 13.8 13.1 5 5.2 10.5 9.3 13.9 6 13.4 6.6 14.2 6.4 7 4.5 11.2 6.2 11.2 8 5.8 3.4 6.0 3.7 My results: set 4: symmetric orthogonalization tanh a1 = 1.0 source1 3.96 dB source2 8.56 dB set 5: symmetric orthogonalization tanh a1 = 1.0 source1 5.93 dB source2 4.05 dB symmetric orthogonalization exp source1 9.76 dB source2 9.86 dB symmetric orthogonalization cubic source1 5.84 dB source2 3.99 dB maximum likelihood estimation exp source1 5.64 dB source2 3.89 dB source1 5.36 dB source2 3.89 dB set 6: symmetric orthogonalization tanh a1 = 1.0 source1 15.5 dB source2 11.8 dB maximum likelihood estimation tanh a1 = 1.0 source1 15.5 dB source2 11.8 dB set 7: symmetric orthogonalization tanh a1 = 1.0 source1 9.93 dB source2 14.8 dB maximum likelihood estimation tanh a1 = 1.0 source1 10.1 dB source2 14.8 dB set 8: symmetric orthogonalization tanh a1 = 1.0 source1 11.5 dB source2 6.91 dB source1 11.3 dB source2 10.9 dB maximum likelihood estimation tanh a1 = 1.0 source1 11.4 dB source2 6.79 dB source1 11.4 dB source2 6.79 dB source1 11.4 dB source2 6.79 dB maximum likelihood estimation exp source1 11.4 dB source2 6.79 dB

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 double	a1
private boolean	blueRequested
static final int	CUBIC_FUNCTION
static final int	DEFLATIONARY_ORTHOGONALIZATION
private ModelImage[]	destImage	DOCUMENT ME!
private double	endTolerance
static final int	EXP_FUNCTION
private boolean	greenRequested
private boolean	haveColor	DOCUMENT ME!
private int	icAlgorithm
private int	icNumber	number of independent components to be retained.
static final int	MAXIMUM_LIKELIHOOD_ESTIMATION
private int	maxIterations
private int	nonlinearFunction
private int	nPlanes	number of image planes present.
private boolean	redRequested
private boolean	selfTest
private ModelImage	srcImage	DOCUMENT ME!
private ModelImage[]	srcImageArray
static final int	SYMMETRIC_ORTHOGONALIZATION
static final int	TANH_FUNCTION

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

destFlag, image25D, mask, maxProgressValue, minProgressValue, multiThreadingEnabled, nthreads, progress, progressModulus, progressStep, runningInSeparateThread, separable, threadStopped

Fields inherited from class java.lang.Thread

MAX_PRIORITY, MIN_PRIORITY, NORM_PRIORITY

Constructor Summary

Constructors

Constructor	Description
AlgorithmIndependentComponents(ModelImage[] destImg, ModelImage[] srcImgArray, int icNumber, int icAlgorithm, int nonlinearFunction, double a1, double endTolerance, int maxIterations, boolean redRequested, boolean greenRequested, boolean blueRequested)	Creates a new AlgorithmIndependentComponents object.
AlgorithmIndependentComponents(ModelImage[] destImg, ModelImage srcImg, int icNumber, int icAlgorithm, int nonlinearFunction, double a1, double endTolerance, int maxIterations, boolean redRequested, boolean greenRequested, boolean blueReuqested)	Creates a new AlgorithmIndependentComponents object.

Method Summary

Modifier and Type	Method	Description
void	finalize()	Prepares this class for destruction.
private void	iComponent()	DOCUMENT ME!
void	runAlgorithm()	Start algorithm.
void	updateFileInfo(ModelImage image, ModelImage resultImage)	Copy important file information to resultant image structure.

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

destImage

private ModelImage[] destImage

DOCUMENT ME!

haveColor

private boolean haveColor

DOCUMENT ME!

nPlanes

private int nPlanes

number of image planes present.

icNumber

private int icNumber

number of independent components to be retained. For SYMMETRIC_ORTHOGONALIZATION and MAXIMUM_LIKELIHOOD_ESTIMATION icNumber = nPlanes = colorsRequested * number of source images for color = number of source images for black and white icNumber can only be varied from 1 to nPlanes with DEFLATIONARY_ORTHOGONALIZATION

srcImageArray

private ModelImage[] srcImageArray

srcImage

private ModelImage srcImage

DOCUMENT ME!

nonlinearFunction

private int nonlinearFunction

a1

private double a1

endTolerance

private double endTolerance

icAlgorithm

private int icAlgorithm

SYMMETRIC_ORTHOGONALIZATION

public static final int SYMMETRIC_ORTHOGONALIZATION

See Also:

• Constant Field Values

DEFLATIONARY_ORTHOGONALIZATION

public static final int DEFLATIONARY_ORTHOGONALIZATION

See Also:

• Constant Field Values

MAXIMUM_LIKELIHOOD_ESTIMATION

public static final int MAXIMUM_LIKELIHOOD_ESTIMATION

See Also:

• Constant Field Values

TANH_FUNCTION

public static final int TANH_FUNCTION

See Also:

• Constant Field Values

EXP_FUNCTION

public static final int EXP_FUNCTION

See Also:

• Constant Field Values

CUBIC_FUNCTION

public static final int CUBIC_FUNCTION

See Also:

• Constant Field Values

maxIterations

private int maxIterations

redRequested

private boolean redRequested

greenRequested

private boolean greenRequested

blueRequested

private boolean blueRequested

selfTest

private boolean selfTest

Constructor Details

AlgorithmIndependentComponents

public AlgorithmIndependentComponents(ModelImage[] destImg,
 ModelImage srcImg,
 int icNumber,
 int icAlgorithm,
 int nonlinearFunction,
 double a1,
 double endTolerance,
 int maxIterations,
 boolean redRequested,
 boolean greenRequested,
 boolean blueReuqested)

Creates a new AlgorithmIndependentComponents object.

Parameters:

destImg - image model where result image is to stored

srcImg - source image model

icNumber - icNumber is the number of independent component images to retain

icAlgorithm -

nonlinearFunction -

a1 -

endTolerance -

maxIterations -

AlgorithmIndependentComponents

public AlgorithmIndependentComponents(ModelImage[] destImg,
 ModelImage[] srcImgArray,
 int icNumber,
 int icAlgorithm,
 int nonlinearFunction,
 double a1,
 double endTolerance,
 int maxIterations,
 boolean redRequested,
 boolean greenRequested,
 boolean blueRequested)

Creates a new AlgorithmIndependentComponents object.

Parameters:

destImg - image model where result image is to stored

icNumber - icNumber is the number of independent component images to retain

icAlgorithm -

nonlinearFunction -

a1 -

endTolerance -

maxIterations -

redRequested -

greenRequested -

blueRequested -

srcImg - source image model

Method Details

finalize

public void finalize()

Prepares this class for destruction.

Overrides:

finalize in class AlgorithmBase

runAlgorithm

public void runAlgorithm()

Start algorithm.

Specified by:

runAlgorithm in class AlgorithmBase

updateFileInfo

public void updateFileInfo(ModelImage image,
 ModelImage resultImage)

Copy important file information to resultant image structure.

Parameters:

image - Source image.

resultImage - Resultant image.

iComponent

private void iComponent()

DOCUMENT ME!