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

All Implemented Interfaces:

java.awt.event.ActionListener, java.awt.event.WindowListener, java.lang.Runnable, java.util.EventListener
```
public class AlgorithmColocalizationEM
extends AlgorithmBase
```
An optional registration may be performed before colocalization. In this registration both images or colors are moved halfway to a common center point. AlgorithmRegOAR2D is used with the cost function being the only registration parameter the user can vary in the dialog box. Correlation ratio is the default, but the user can also select least squares, normalized cross correlation, or normalized mutual information. The colocalization will be performed on the registered image or images rather than on the original image. If the maximum data range is decreased, the bin1 or bin2 will be decreased if necessary to keep them from exceeding the actual number of channels in the data range.
A histogram buffer is generated with bin1 counts along the x axis and bin2 counts along the y axis. bin1 and bin2 are user selectable in the dialog. leftPad, rightPad, bottomPad, and topPad spaces are left around the data area in histBuffer to allow room for axis lines and labels.

The user specifies the number of gaussian distributions. For each distribution the initial values of the means, standard deviations, and correlation are assigned using a random number generator. Each distribution is initially assigned an equal probability.

The ellipse corresponds to a contour of constant probabliity density on which the density is smaller by a factor of exp[-0.5] than it is at the mean point. The probability density function is a jointly gaussian or bivariate gaussian density function. In each iteration: 1.) Find the gaussian distribution j with the form: p(x,y/j) = (1/(2*PI*stdxj*stdyj*sqrt(1 - rhoj**2))) * exp{(-1/(2*(1 - rhoj**2)))[((x-meanxj)**2)/(stdxj**2) - (2*rhoj*(x-meanxj)*(y-meanyj))/(stdxj*stdyj) + ((y - meanyj)**2)/(stdyj**2)]} where stdxj and stdyj are the x and y standard deviations and rhoj is the correlation coefficient.

2.) p(x,y) are found from p(x,y) = sum over j of p(x,y/j)*p(j). 3.) Pold(j/(x,y) = (sum over all j of Pold(x,y/j)*Pold(j))/Pold(x,y)

The update equations for the values of the new variables for each gaussian distribution j are: 4.) meanxj = (sum over x,y of Pold(j/(x,y))*x)/(sum over x,y of Pold(j/(x,y))) 5.) meanyj = (sum over x,y of Pold(j/(x,y))*y)/(sum over x,y of Pold(j/(x,y))) 6.) stdxj**2 = (sum over x,y of Pold(j/(x,y)) * (x - meanxj)**2)/ (sum over x,y of Pold(j/(x,y))) 7.) stdyj**2 = (sum over x,y of Pold(j/(x,y)) * (y - meanyj)**2)/ (sum over x,y of Pold(j/(x,y))) 8.) rhoj = (sum over x,y of Pold(j/(x,y)) * (x - meanxj) * (y - meanyj)/(stdjx * stdjy))/ (sum over x,y of Pold(j/(x,y)))

9.) Each new p[j] is found from (sum over x,y of Pold(j/(x,y)))/number of (x,y) points These 9 basic steps are iterated the number of times specified by the user.

The calculated data is passed to ViewJFrameColocalization.

References:

1.) Expectation Maximization Algorithm by Ismet Bayraktaroglu (ismet@bayraktaroglu.org) Code for circles was originally found at http://www-cse.ucsd.edu/users/ibayrakt/java/em but this site is no longer available.

2.) Neural Networks for Pattern Recognition by Christopher M. Bishop, Oxford University Press, 1995, Section 2.6.2 The EM algorithm, pp. 65 - 72.

3.) Probability, Random Variables, and Random Signal Principles, Second Edition by Peyton Z. Peebles, Jr., McGraw-Hill Book Company, 1987, Section 5.3 Jointly Gaussian Random Variables, pp. 124 - 128.

4.) Clustering Algorithms by John A. Hartigan, Wiley, John & Sons, Incorporated, 1975, Chapter 5, Mixtures, pp. 113 - 129.

5.) Principles of Data Mining by David Hand, Heikki Mannila, and Padhraic Smith, A Bradford Book The MIT Press, 2001, pp. 260 - 265, 281 - 284, 315 - 323.

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
`(package private) float[]`	`b1`	DOCUMENT ME!
`(package private) float[]`	`b2`	DOCUMENT ME!
`private ModelImage`	`baseImage`	DOCUMENT ME!
`private int`	`bin1`	DOCUMENT ME!
`private int`	`bin2`	DOCUMENT ME!
`private int`	`bottomPad`	DOCUMENT ME!
`(package private) float[]`	`buffer`	DOCUMENT ME!
`private int`	`cost`	DOCUMENT ME!
`private int`	`count`	DOCUMENT ME!
`private double[]`	`dMax`	DOCUMENT ME!
`private double[]`	`dMin`	DOCUMENT ME!
`private boolean`	`doOr`	If true, or thresholds.
`private boolean`	`entireImage`	DOCUMENT ME!
`private int`	`gaussians`	DOCUMENT ME!
`private double[]`	`halfMajor`	DOCUMENT ME!
`private double[]`	`halfMinor`	DOCUMENT ME!
`(package private) java.util.BitSet`	`inputMask`	DOCUMENT ME!
`private int`	`iterations`	DOCUMENT ME!
`private int`	`leftPad`	Space padding around the histogram data area.
`private double[][]`	`mean`	DOCUMENT ME!
`private double[][]`	`meanOld`	DOCUMENT ME!
`private byte[]`	`mostProbableClass`	DOCUMENT ME!
`private double[]`	`prob`	DOCUMENT ME!
`private double[]`	`probOld`	DOCUMENT ME!
`private double[]`	`probX`	DOCUMENT ME!
`private double[]`	`probXOld`	DOCUMENT ME!
`private boolean`	`register`	DOCUMENT ME!
`private double[]`	`rho`	DOCUMENT ME!
`private double[]`	`rhoOld`	DOCUMENT ME!
`private int`	`rightPad`	DOCUMENT ME!
`(package private) float[]`	`secondBuffer`	DOCUMENT ME!
`private byte[]`	`segBuffer`	DOCUMENT ME!
`private ModelImage`	`segImage`	Displays classes to which pixels belong.
`private double[][]`	`std`	DOCUMENT ME!
`private double[][]`	`stdOld`	DOCUMENT ME!
`private double[]`	`theta`	DOCUMENT ME!
`private float`	`threshold1`	The minimum values that are considered as data values.
`private float`	`threshold2`	DOCUMENT ME!
`private int`	`topPad`	DOCUMENT ME!
`private boolean`	`useBlue`	DOCUMENT ME!
`private boolean`	`useGreen`	DOCUMENT ME!
`private boolean`	`useRed`	DOCUMENT ME!
`(package private) int`	`xDim`	DOCUMENT ME!
`(package private) int`	`yDim`	DOCUMENT ME!
`(package private) int`	`zDim`	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
`AlgorithmColocalizationEM(ModelImage destImg, ModelImage segImage, ModelImage srcImg, int bin1, int bin2, float threshold1, float threshold2, boolean doOr, int leftPad, int rightPad, int bottomPad, int topPad, boolean useRed, boolean useGreen, boolean useBlue, boolean entireImage, boolean register, int cost, int gaussians, int iterations)`	Constructor for images in which 2D histogram is placed in a predetermined destination image.
`AlgorithmColocalizationEM(ModelImage destImg, ModelImage segImage, ModelImage srcImg, ModelImage baseImage, int bin1, int bin2, float threshold1, float threshold2, boolean doOr, int leftPad, int rightPad, int bottomPad, int topPad, boolean entireImage, boolean register, int cost, int gaussians, int iterations)`	Constructor for images in which 2D histogram is placed in a predetermined destination image.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`private void`	`calc2D()`	This function produces a 2D histogram image with srcImage values represented across the x axis and baseImage values represented across the y axis.
`private void`	`calc3D()`	This function produces a 2D histogram image with srcImage values represented across the x axis and baseImage values represented across the y axis.
`private void`	`calcColor2D()`	This function produces a 2D histogram image with first color values represented across the x axis and second color values represented across the y axis.
`private void`	`calcColor3D()`	This function produces a 2D histogram image with first color values represented across the x axis and second color values represented across the y axis.
`private void`	`emfunction()`	DOCUMENT ME!
`void`	`finalize()`	Prepares this class for destruction.
`private void`	`getProbX()`	DOCUMENT ME!
`private void`	`getProbXOld()`	DOCUMENT ME!
`private void`	`newToOld()`	DOCUMENT ME!
`private double`	`probjxOld(int i, int j)`	DOCUMENT ME!
`private double`	`probXj(int i, int j)`	DOCUMENT ME!
`private double`	`probXjOld(int i, int j)`	DOCUMENT ME!
`void`	`runAlgorithm()`	starts the 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, 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
  - b1
```
float[] b1
```
    DOCUMENT ME!
  - b2
```
float[] b2
```
    DOCUMENT ME!
  - buffer
```
float[] buffer
```
    DOCUMENT ME!
  - inputMask
```
java.util.BitSet inputMask
```
    DOCUMENT ME!
  - secondBuffer
```
float[] secondBuffer
```
    DOCUMENT ME!
  - xDim
```
int xDim
```
    DOCUMENT ME!
  - yDim
```
int yDim
```
    DOCUMENT ME!
  - zDim
```
int zDim
```
    DOCUMENT ME!
  - baseImage
```
private ModelImage baseImage
```
    DOCUMENT ME!
  - bin1
```
private int bin1
```
    DOCUMENT ME!
  - bin2
```
private int bin2
```
    DOCUMENT ME!
  - bottomPad
```
private int bottomPad
```
    DOCUMENT ME!
  - cost
```
private int cost
```
    DOCUMENT ME!
  - count
```
private int count
```
    DOCUMENT ME!
  - dMax
```
private double[] dMax
```
    DOCUMENT ME!
  - dMin
```
private double[] dMin
```
    DOCUMENT ME!
  - doOr
```
private boolean doOr
```
    If true, or thresholds. If false, and thresholds
  - entireImage
```
private boolean entireImage
```
    DOCUMENT ME!
  - gaussians
```
private int gaussians
```
    DOCUMENT ME!
  - halfMajor
```
private double[] halfMajor
```
    DOCUMENT ME!
  - halfMinor
```
private double[] halfMinor
```
    DOCUMENT ME!
  - iterations
```
private int iterations
```
    DOCUMENT ME!
  - leftPad
```
private int leftPad
```
    Space padding around the histogram data area.
  - mean
```
private double[][] mean
```
    DOCUMENT ME!
  - meanOld
```
private double[][] meanOld
```
    DOCUMENT ME!
  - mostProbableClass
```
private byte[] mostProbableClass
```
    DOCUMENT ME!
  - prob
```
private double[] prob
```
    DOCUMENT ME!
  - probOld
```
private double[] probOld
```
    DOCUMENT ME!
  - probX
```
private double[] probX
```
    DOCUMENT ME!
  - probXOld
```
private double[] probXOld
```
    DOCUMENT ME!
  - register
```
private boolean register
```
    DOCUMENT ME!
  - rho
```
private double[] rho
```
    DOCUMENT ME!
  - rhoOld
```
private double[] rhoOld
```
    DOCUMENT ME!
  - rightPad
```
private int rightPad
```
    DOCUMENT ME!
  - segBuffer
```
private byte[] segBuffer
```
    DOCUMENT ME!
  - segImage
```
private ModelImage segImage
```
    Displays classes to which pixels belong.
  - std
```
private double[][] std
```
    DOCUMENT ME!
  - stdOld
```
private double[][] stdOld
```
    DOCUMENT ME!
  - theta
```
private double[] theta
```
    DOCUMENT ME!
  - threshold1
```
private float threshold1
```
    The minimum values that are considered as data values.
  - threshold2
```
private float threshold2
```
    DOCUMENT ME!
  - topPad
```
private int topPad
```
    DOCUMENT ME!
  - useBlue
```
private boolean useBlue
```
    DOCUMENT ME!
  - useGreen
```
private boolean useGreen
```
    DOCUMENT ME!
  - useRed
```
private boolean useRed
```
    DOCUMENT ME!
- Constructor Detail
  - AlgorithmColocalizationEM
```
public AlgorithmColocalizationEM(ModelImage destImg,
                                 ModelImage segImage,
                                 ModelImage srcImg,
                                 ModelImage baseImage,
                                 int bin1,
                                 int bin2,
                                 float threshold1,
                                 float threshold2,
                                 boolean doOr,
                                 int leftPad,
                                 int rightPad,
                                 int bottomPad,
                                 int topPad,
                                 boolean entireImage,
                                 boolean register,
                                 int cost,
                                 int gaussians,
                                 int iterations)
```
    Constructor for images in which 2D histogram is placed in a predetermined destination image. Used for 2 black and white images
    
    Parameters:
    
    destImg - image model where result image is to stored
    
    segImage - image to display classes to which pixels belong
    
    srcImg - image for x axis of histogram
    
    baseImage - image for y axis of histogram
    
    bin1 - histogram x axis bin number
    
    bin2 - histogram y axis bin number
    
    threshold1 - minimum value for data in sourceImage
    
    threshold2 - minimum value for data in baseImage
    
    doOr - If true, or thresholds. If false, and thresholds.
    
    leftPad - space to the left of the histogram bin area
    
    rightPad - space to the right of the histogram bin area
    
    bottomPad - space to the bottom of the histogram bin area
    
    topPad - space to the top of the histogram bin area
    
    entireImage - If false, only use data from VOI region
    
    register - If true register 2 black and white images before colocalization
    
    cost - Cost function used in registration
    
    gaussians - Number of gaussians
    
    iterations - Number of iterations
  - AlgorithmColocalizationEM
```
public AlgorithmColocalizationEM(ModelImage destImg,
                                 ModelImage segImage,
                                 ModelImage srcImg,
                                 int bin1,
                                 int bin2,
                                 float threshold1,
                                 float threshold2,
                                 boolean doOr,
                                 int leftPad,
                                 int rightPad,
                                 int bottomPad,
                                 int topPad,
                                 boolean useRed,
                                 boolean useGreen,
                                 boolean useBlue,
                                 boolean entireImage,
                                 boolean register,
                                 int cost,
                                 int gaussians,
                                 int iterations)
```
    Constructor for images in which 2D histogram is placed in a predetermined destination image. Used for 1 color image.
    
    Parameters:
    
    destImg - image model where result image is to stored
    
    segImage - image to display classes to which pixels belong
    
    srcImg - source image model
    
    bin1 - histogram x axis bin number
    
    bin2 - histogram y axis bin number
    
    threshold1 - minimum data value for first color
    
    threshold2 - minimum data value for second color
    
    doOr - If true, or thresholds. If false, and thresholds.
    
    leftPad - space to the left of the histogram bin area
    
    rightPad - space to the right of the histogram bin area
    
    bottomPad - space to the bottom of the histogram bin area
    
    topPad - space to the top of the histogram bin area
    
    useRed - DOCUMENT ME!
    
    useGreen - DOCUMENT ME!
    
    useBlue - DOCUMENT ME!
    
    entireImage - If false, use only data from VOI region
    
    register - If true, register 2 colors before colocalization
    
    cost - Cost function used in registration
    
    gaussians - Number of gaussians
    
    iterations - Number of iterations
- Method Detail
  - finalize
```
public void finalize()
```
    Prepares this class for destruction.
    
    Overrides:
    
    finalize in class AlgorithmBase
  - runAlgorithm
```
public void runAlgorithm()
```
    starts the 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.
  - calc2D
```
private void calc2D()
```
    This function produces a 2D histogram image with srcImage values represented across the x axis and baseImage values represented across the y axis.
  - calc3D
```
private void calc3D()
```
    This function produces a 2D histogram image with srcImage values represented across the x axis and baseImage values represented across the y axis.
  - calcColor2D
```
private void calcColor2D()
```
    This function produces a 2D histogram image with first color values represented across the x axis and second color values represented across the y axis.
  - calcColor3D
```
private void calcColor3D()
```
    This function produces a 2D histogram image with first color values represented across the x axis and second color values represented across the y axis.
  - emfunction
```
private void emfunction()
```
    DOCUMENT ME!
  - getProbX
```
private void getProbX()
```
    DOCUMENT ME!
  - getProbXOld
```
private void getProbXOld()
```
    DOCUMENT ME!
  - newToOld
```
private void newToOld()
```
    DOCUMENT ME!
  - probjxOld
```
private double probjxOld(int i,
                         int j)
```
    DOCUMENT ME!
    
    Parameters:
    
    i - DOCUMENT ME!
    
    j - DOCUMENT ME!
    
    Returns:
    
    DOCUMENT ME!
  - probXj
```
private double probXj(int i,
                      int j)
```
    DOCUMENT ME!
    
    Parameters:
    
    i - DOCUMENT ME!
    
    j - DOCUMENT ME!
    
    Returns:
    
    DOCUMENT ME!
  - probXjOld
```
private double probXjOld(int i,
                         int j)
```
    DOCUMENT ME!
    
    Parameters:
    
    i - DOCUMENT ME!
    
    j - DOCUMENT ME!
    
    Returns:
    
    DOCUMENT ME!

Class AlgorithmColocalizationEM

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

b1

b2

buffer

inputMask

secondBuffer

xDim

yDim

zDim

baseImage

bin1

bin2

bottomPad

cost

count

dMax

dMin

doOr

entireImage

gaussians

halfMajor

halfMinor

iterations

leftPad

mean

meanOld

mostProbableClass

prob

probOld

probX

probXOld

register

rho

rhoOld

rightPad

segBuffer

segImage

std

stdOld

theta

threshold1

threshold2

topPad

useBlue

useGreen

useRed

Constructor Detail

AlgorithmColocalizationEM

AlgorithmColocalizationEM

Method Detail

finalize

runAlgorithm

updateFileInfo

calc2D

calc3D

calcColor2D

calcColor3D

emfunction

getProbX

getProbXOld

newToOld

probjxOld

probXj

probXjOld