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

All Implemented Interfaces:

java.awt.event.ActionListener, java.awt.event.WindowListener, java.lang.Runnable, java.util.EventListener
```
public class AlgorithmRegularizedIsotropicDiffusion
extends AlgorithmBase
```
Algorithm to apply Regularized Isotropic Nonlinear Diffusion as described by:
Joachim Weickert Chapter 15 in Handbook of Computer Vision and Applications, Volume 2 Signal Processing and Pattern Recognition. The book is published by Academic Press, ISBN 0-12-379772-1

Here is a listing of the author's terminology used in the title.
- Regularized the computed image is blurred with a Gaussian kernel prior to taking finite derivatives
- Isotropic the gradient and flux are parallel
- Nonlinear the weights used for combining pixels change over the image, space variant.
- Diffusion this model uses the heat equation
Version:

1.0; 31, July 2003

Author:

Paul F. Hemler, Ph.D.

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 AlgorithmSeparableConvolver`	`algoSepConvolver`	Handle to the separable convolution kernel.
`private AlgorithmSeparableConvolver`	`algoSepConvolverB`	Handle to the separable convolution kernel.
`private AlgorithmSeparableConvolver`	`algoSepConvolverG`	Handle to the separable convolution kernel.
`private AlgorithmSeparableConvolver`	`algoSepConvolverR`	Handle to the separable convolution kernel.
`private float`	`contrast`	Diffusion contrast parameter.
`private boolean`	`do25D`	Flag indicating 2.5D processing.
`private float`	`lambda`	Contrast multiplied by the maximum gradient.
`(package private) int`	`numIterations`	The number of iterations.
`private float[]`	`sigmas`	Standard deviations of the gaussian used to calculate the kernels.
`private float`	`stdDev`	Standard deviation used for the Gaussians.
`(package private) float`	`timeStep`	The time parameter step size, called tau in the algorithm description In 2D for stability the time step should be
`private float[]`	`xDataRound`	Storage location for the 1D gaussian kernels.
`private int`	`xDim`	Image dimensions.
`private float[]`	`yDataRound`	Storage location for the 1D gaussian kernels.
`private int`	`yDim`	Image dimensions.
`private float[]`	`zDataRound`	Storage location for the 1D gaussian kernels.
`private int`	`zDim`	Image dimensions.

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
`AlgorithmRegularizedIsotropicDiffusion(ModelImage destImg, ModelImage srcImg, int numI, float stdDev, float contrast, boolean do25D)`	Creates a new AlgorithmRegularizedIsotropicDiffusion object.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`private float`	`diffusivity(float d)`	Returns the weighting for values used in computing new (diffused) pixel values.
`void`	`finalize()`	Prepares this class for destruction.
`private float`	`getVal(float[] buffer, int col, int row)`	Returns the value in a 2D image buffer of the pixel at location col, row.
`private float`	`getVal(float[] buffer, int col, int row, int slice)`	Returns the value in a 3D image buffer of the pixel at location col, row, slice.
`private void`	`gradientMagnitude(float[] img, float[] mag)`	Computes the gradient magnitude of the buffer passed in.
`private void`	`gradientMagnitude3D(float[] img, float[] mag)`	Computes the gradient magnitude of the buffer passed in.
`private void`	`makeKernels1D(boolean do3D)`	DOCUMENT ME!
`private void`	`run2D(int numImages)`	Iterates the Regularized Isotropic Nonlinear Diffusion algorithm for 2D and 2.5D images.
`private void`	`run2DC(int numImages)`	Iterates the Regularized Isotropic Nonlinear Diffusion algorithm for 2D and 2.5D color images.
`private void`	`run3D()`	Iterates the Regularized Isotropic Nonlinear Diffusion algorithm for 3D images.
`private void`	`run3DC()`	Iterates the Regularized Isotropic Nonlinear Diffusion algorithm for 3D color images.
`void`	`runAlgorithm()`	Starts the program.
`private void`	`upDateImage(float[] resltBuffer, float[] srcBuffer, float[] gradBuffer)`	DOCUMENT ME!
`private void`	`upDateImage3D(float[] resltBuffer, float[] srcBuffer, float[] gradBuffer)`	DOCUMENT ME!

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
  - numIterations
```
int numIterations
```
    The number of iterations.
  - timeStep
```
float timeStep
```
    The time parameter step size, called tau in the algorithm description In 2D for stability the time step should be
    
    algoSepConvolver
    
    private AlgorithmSeparableConvolver algoSepConvolver
    
    Handle to the separable convolution kernel.
    
    algoSepConvolverB
    
    private AlgorithmSeparableConvolver algoSepConvolverB
    
    Handle to the separable convolution kernel.
    
    algoSepConvolverG
    
    private AlgorithmSeparableConvolver algoSepConvolverG
    
    Handle to the separable convolution kernel.
    
    algoSepConvolverR
    
    private AlgorithmSeparableConvolver algoSepConvolverR
    
    Handle to the separable convolution kernel.
    
    contrast
    
    private float contrast
    
    Diffusion contrast parameter.
    
    do25D
    
    private boolean do25D
    
    Flag indicating 2.5D processing.
    
    lambda
    
    private float lambda
    
    Contrast multiplied by the maximum gradient.
    
    sigmas
    
    private float[] sigmas
    
    Standard deviations of the gaussian used to calculate the kernels.
    
    stdDev
    
    private float stdDev
    
    Standard deviation used for the Gaussians.
    
    xDataRound
    
    private float[] xDataRound
    
    Storage location for the 1D gaussian kernels.
    
    xDim
    
    private int xDim
    
    Image dimensions.
    
    yDim
    
    private int yDim
    
    Image dimensions.
    
    zDim
    
    private int zDim
    
    Image dimensions.
    
    yDataRound
    
    private float[] yDataRound
    
    Storage location for the 1D gaussian kernels.
    
    zDataRound
    
    private float[] zDataRound
    
    Storage location for the 1D gaussian kernels.
    
    Constructor Detail
    
    AlgorithmRegularizedIsotropicDiffusion
    
    public AlgorithmRegularizedIsotropicDiffusion(ModelImage destImg, ModelImage srcImg, int numI, float stdDev, float contrast, boolean do25D)
    
    Creates a new AlgorithmRegularizedIsotropicDiffusion object.
    
    Parameters:
    
    destImg - ModelImage image model where result image is to stored
    
    srcImg - ModelImage source image model
    
    numI - int number of iterations
    
    stdDev - float standard deviation used in the Gaussians
    
    contrast - float diffusion contrast parameter
    
    do25D - boolean If true, process each slice separately
    
    Method Detail
    
    finalize
    
    public void finalize()
    
    Prepares this class for destruction.
    
    Overrides:
    
    finalize in class AlgorithmBase
    
    runAlgorithm
    
    public void runAlgorithm()
    
    Starts the program.
    
    Specified by:
    
    runAlgorithm in class AlgorithmBase
    
    diffusivity
    
    private float diffusivity(float d)
    
    Returns the weighting for values used in computing new (diffused) pixel values.
    
    Parameters:
    
    d - float Value to compute the diffusivity
    
    Returns:
    
    float The value of the diffusivity corresponding to the d value
    
    getVal
    
    private float getVal(float[] buffer, int col, int row)
    
    Returns the value in a 2D image buffer of the pixel at location col, row.
    
    Parameters:
    
    buffer - float[] The image buffer
    
    col - int Column index
    
    row - int Row index
    
    Returns:
    
    float The pixel value
    
    getVal
    
    private float getVal(float[] buffer, int col, int row, int slice)
    
    Returns the value in a 3D image buffer of the pixel at location col, row, slice.
    
    Parameters:
    
    buffer - float[] The image buffer
    
    col - int Column index
    
    row - int Row index
    
    slice - int Slice index
    
    Returns:
    
    float The pixel value
    
    gradientMagnitude
    
    private void gradientMagnitude(float[] img, float[] mag)
    
    Computes the gradient magnitude of the buffer passed in. This version computes values for the image borders and corners
    
    Parameters:
    
    img - float [] source buffer
    
    mag - float [] a buffer where the gradient magnitude will be placed
    
    gradientMagnitude3D
    
    private void gradientMagnitude3D(float[] img, float[] mag)
    
    Computes the gradient magnitude of the buffer passed in. This version computes values for the image borders and corners
    
    Parameters:
    
    img - float [] source buffer
    
    mag - float [] a buffer where the gradient magnitude will be placed
    
    makeKernels1D
    
    private void makeKernels1D(boolean do3D)
    
    DOCUMENT ME!
    
    Parameters:
    
    do3D - boolean Flag indicating if a 3D kernel should be computed
    
    run2D
    
    private void run2D(int numImages)
    
    Iterates the Regularized Isotropic Nonlinear Diffusion algorithm for 2D and 2.5D images.
    
    Parameters:
    
    numImages - int number of images to be blurred. If 2D image then nImage = 1, if 3D image where each slice is to processed independently then nImages equals the number of slices in the volume.
    
    run2DC
    
    private void run2DC(int numImages)
    
    Iterates the Regularized Isotropic Nonlinear Diffusion algorithm for 2D and 2.5D color images.
    
    Parameters:
    
    numImages - int number of slices to be blurred. If 2D image then nImage = 1, if 3D image where each slice is to processed independently then nImages equals the number of slices in the volume.
    
    run3D
    
    private void run3D()
    
    Iterates the Regularized Isotropic Nonlinear Diffusion algorithm for 3D images.
    
    run3DC
    
    private void run3DC()
    
    Iterates the Regularized Isotropic Nonlinear Diffusion algorithm for 3D color images.
    
    upDateImage
    
    private void upDateImage(float[] resltBuffer, float[] srcBuffer, float[] gradBuffer)
    
    DOCUMENT ME!
    
    Parameters:
    
    resltBuffer - float [] The preallocated result buffer
    
    srcBuffer - float [] The initialized source buffer containing the input image
    
    gradBuffer - float [] The initialized buffer containing the magnitude of the gradient of the Gaussian blurred input image
    
    upDateImage3D
    
    private void upDateImage3D(float[] resltBuffer, float[] srcBuffer, float[] gradBuffer)
    
    DOCUMENT ME!
    
    Parameters:
    
    resltBuffer - float [] The preallocated result buffer
    
    srcBuffer - float [] The initialized source buffer containing the input image
    
    gradBuffer - float [] The initialized buffer containing the magnitude of the gradient of the Gaussian blurred input image

Class AlgorithmRegularizedIsotropicDiffusion

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

numIterations

timeStep

algoSepConvolver

algoSepConvolverB

algoSepConvolverG

algoSepConvolverR

contrast

do25D

lambda

sigmas

stdDev

xDataRound

xDim

yDim

zDim

yDataRound

zDataRound

Constructor Detail

AlgorithmRegularizedIsotropicDiffusion

Method Detail

finalize

runAlgorithm

diffusivity

getVal

getVal

gradientMagnitude

gradientMagnitude3D

makeKernels1D

run2D

run2DC

run3D

run3DC

upDateImage

upDateImage3D