java.lang.Object
- java.lang.Thread
- - gov.nih.mipav.model.algorithms.AlgorithmBase
  - - gov.nih.mipav.model.algorithms.registration.AlgorithmRegELSUNCOAR35D

All Implemented Interfaces:

java.awt.event.ActionListener, java.awt.event.WindowListener, java.lang.Runnable, java.util.EventListener
```
public class AlgorithmRegELSUNCOAR35D
extends AlgorithmBase
```
This is an automatic registration method based on FLIRT. FLIRT stands for FMRIB's Linear Image Registration Tool. For more information on FLIRT, visit their homepage at http://www.fmrib.ox.ac.uk/fsl/flirt/. Their main paper is:
Jenkinson, M. and Smith, S. (2001a).
A global optimisation method for robust affine registration of brain images.
Medical Image Analysis, 5(2):143-156.

Internal registration is performed within one 4D image.

In adjacent mode the first volume above the reference volume is registered to the reference volume, then the second volume above the reference volume is registered to the first volume above the reference volume, and so on until the last volume is registered the next to last volume. Then, the first volume below the reference volume is registered to the reference volume, the second volume below the reference volume is registered to first volume below the reference volume, and so on until the first volume is registered to the second volume. In reference mode every volume is simply registered to the reference volume. In average mode every volume is simply registered to the average of all the volumes.

Our algorithm works as follows:
1.) We find the minimum resolution of the images.
2.) We transform the images into isotropic voxels.
3.) We subsample the images by 2, 4, and 8 or 16, depending on the resolution.
Subsampling can be in x, y, and z or in only x and y. We loop thru a number of iterations equal to the number of volumes - 1 for adjacent mode or equal to the number of volumes in average or reference mode with one volume as the input volume and one volume as the reference volume.
For each iteration:
4.) With the volumes that were subsampled by 8 or 16, we call levelEight. This function will use the coarse sampling rate and optimize translations and global scale at the given rotation. So for example, if the coarse sampling range were -30 to 30 at every 15 degrees, we would optimize at rotations of -30, -15, 0, 15, 30.
5.) Still in levelEight, we now measure the cost at the fine sampling rate. We interpolate the translations and global scale to come up with a good guess as to what the optimized translation would be at that point.
6.) We take the top 20% of the points and optimize them.
7.) We now have a large multi-array of costs. 20% of those have been optimized and placed back into their original position in the multi-array. We look at the 9 neighbors of a point: +, =, or - one fine sample. If our point has a cost greater than any of these, it is not a minima. Otherwise it is. We save it in a vector of minima.
8.) We optimize the minima over rotation as well as translations and global scale. (Previously we had not optimized over rotation.) We return two vectors, one containing the minima before optimization, one containing the minima after optimization.
9.) We now call levelFour with the volumes subsampled by 4 and the vectors of minima. We measure the costs of the minima on the new volumes and sort them. We take the top numMinima in each vector (pre-optimization and post-optimization) and optimize them. We put them all into one vector.
10.) We perturb the rotation by zero and by plus-minus fineDelta. If it's not a rigid transformation, we then perturb the global scaling by factors of 0.8, 0.9, 1.0, 1.1, and 1.2.
11.) We optimize the perturbations. We return a vector of the perturbed, optimized minima.
12.) We now call levelTwo with the volumes subsampled by 2. We measure the costs of the minima at the new volumes. We optimize the best minimum with 7 degrees of freedom, then 9, then 12. If the user has limited the degrees of freedom to 6, there will only be one optimization run, with 6 degrees of freedom. The function returns the best minimum after optimization.
13.) We call levelOne with the un-subsampled volumes. At levelOne, one optimization run is performed, with the maximum allowable degrees of freedom, as specified by the user (the max is 12).
14.) The best answer is returned from levelOne. The inverse of the matrix from this answer is used to register the input volume to the reference volume. The registered input volume data is imported into inputImage.

Only subsample if 16 or more z slices are present, so that the number of z slices will not be reduced below 8.

Author:

Matthew McAuliffe, Neva Cherniavsky, Benjamin Link - Added code to use less memory. (April 2003)

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`(package private) class`	`AlgorithmRegELSUNCOAR35D.MatrixListItem`	Helper class to make it easy to store the necessary information about a minimum.

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 static int`	`ADJACENT`	DOCUMENT ME!
`private boolean`	`allowLevel16XY`	DOCUMENT ME!
`private boolean`	`allowLevel16Z`	DOCUMENT ME!
`private boolean`	`allowLevel2XY`	Flags are true if weighted image is not present or if weighted image subsampling occurred, false if weighted image subsampling did not occur.
`private boolean`	`allowLevel2Z`	DOCUMENT ME!
`private boolean`	`allowLevel4XY`	DOCUMENT ME!
`private boolean`	`allowLevel4Z`	DOCUMENT ME!
`private boolean`	`allowLevel8XY`	DOCUMENT ME!
`private boolean`	`allowLevel8Z`	DOCUMENT ME!
`private AlgorithmRegELSUNCOAR35D.MatrixListItem`	`answer`	Final answer after registration.
`private static int`	`AVERAGE`	DOCUMENT ME!
`private int`	`baseNumIter`	Limits number of iterations in elsunc optimization. maxIter in the call to elsunc will be an integer multiple of baseNumIter
`private float[]`	`buffer`	DOCUMENT ME!
`private float[]`	`bufferA`	DOCUMENT ME!
`private float[]`	`bufferIW`	DOCUMENT ME!
`private float[]`	`bufferW`	DOCUMENT ME!
`private boolean`	`calcCOG`	If true calculate the center of gravity (mass) and use the difference to intialize the translation.
`private int`	`coarseNum`	Number of passes that will be made in the coarse sampling and fine sampling.
`private float`	`coarseRate`	Coarse and fine sampling parameters.
`private int`	`colorFactor`	1 for black and white, 4 for color.
`private int`	`costChoice`	Choice of which cost function to use.
`private boolean`	`doColor`	DOCUMENT ME!
`private int`	`DOF`	Maximum degrees of freedom when running the optimization.
`private boolean`	`doGraph`	Produce 2 output graphs - 1 for 3 rotations and 1 for 3 translations.
`private boolean`	`doMultiThread`
`private boolean`	`doSubsample`	if true subsample.
`private boolean`	`fastMode`	If true this algorithm skips all subsample and goes directly to the level 1 optimization.
`private int`	`fineNum`	Number of passes that will be made in the coarse sampling and fine sampling.
`private float`	`fineRate`	Coarse and fine sampling parameters.
`private ModelImage`	`imageWeightIso`	DOCUMENT ME!
`private ModelImage`	`input1`	Image used to import a volume from inputImage.
`private ModelImage`	`inputImage`	This is the image in which internal registration will be performed.
`private ModelImage`	`inputw_1`	Image used to import a volume from inputWeight.
`private ModelImage`	`inputWeight`	This gives weights for the input image - higher weights mean a greater impact in that area on the registration.
`private int`	`interp`	Interpolation method used in transformations.
`private int`	`interp2`	Interpolation method used in output.
`private float[]`	`iResols`	The voxel resolutions of the input image.
`private ModelImage`	`isoImage`	new to this version: must keep isoImage around if image needed to be transformed.
`private float`	`level1FactorXY`	Multiplication factor for level 1 - will be set based on subsampling.
`private float`	`level1FactorZ`	DOCUMENT ME!
`private float`	`level2FactorXY`	Multiplication factor for level 2 - will be set based on subsampling.
`private float`	`level2FactorZ`	DOCUMENT ME!
`private float`	`level4FactorXY`	Multiplication factor for level 4 - will be set based on subsampling.
`private float`	`level4FactorZ`	DOCUMENT ME!
`private int`	`maxDim`	DOCUMENT ME!
`private int`	`maxIter`	Limits number of iterations in elsunc optimization. maxIter in the call to elsunc will be an integer multiple of baseNumIter
`private static int`	`minimumZForSub`	DOCUMENT ME!
`private int`	`mode`	DOCUMENT ME!
`private int`	`numMinima`	Number of minima from level 8 to test at level 4.
`private ModelImage`	`output_1`	DOCUMENT ME!
`private ModelImage`	`outsidePreReferenceVolume`	DOCUMENT ME!
`private ModelImage`	`outsideReferenceVolume`	DOCUMENT ME!
`private int`	`refImageNo`	Indicates the first volume used as a reference volume, if mode == ADJACENT.
`private boolean`	`regToAdjImage`	If true use adjacent image for registration.
`private boolean`	`resample`	true if resolutions unequal, false if resolutions equal.
`private boolean`	`resampleW`	true if weight image is resampled.
`private float[][]`	`rot`	Arrays used for producing graphs for DOF == 6 and register to average or reference image.
`private float`	`rotateBegin`	Coarse and fine sampling parameters.
`private float`	`rotateEnd`	Coarse and fine sampling parameters.
`private int`	`searchAlgorithm`
`private ModelSimpleImage`	`simpleInput_1`	Simple version of an image volume of the input image.
`private ModelSimpleImage`	`simpleInputSub16_1`	Simple version of an image volume of the input image, subsampled by 16.
`private ModelSimpleImage`	`simpleInputSub2_1`	Simple version of an image volume of the input image, subsampled by 2.
`private ModelSimpleImage`	`simpleInputSub4_1`	Simple version of an image volume of the input image, subsampled by 4.
`private ModelSimpleImage`	`simpleInputSub8_1`	Simple version of an image volume of the input image, subsampled by 8.
`private ModelSimpleImage`	`simpleRef_1`	Simple version of input image.
`private ModelSimpleImage`	`simpleRefSub16_1`	Simple version of input image, subsampled by 16.
`private ModelSimpleImage`	`simpleRefSub2_1`	Simple version of input image, subsampled by 2.
`private ModelSimpleImage`	`simpleRefSub4_1`	Simple version of input image, subsampled by 4.
`private ModelSimpleImage`	`simpleRefSub8_1`	Simple version of input image, subsampled by 8.
`private ModelSimpleImage`	`simpleWeightI_1`	DOCUMENT ME!
`private ModelSimpleImage`	`simpleWeightISub16_1`	DOCUMENT ME!
`private ModelSimpleImage`	`simpleWeightISub2_1`	DOCUMENT ME!
`private ModelSimpleImage`	`simpleWeightISub4_1`	DOCUMENT ME!
`private ModelSimpleImage`	`simpleWeightISub8_1`	DOCUMENT ME!
`private ModelSimpleImage`	`simpleWeightR_1`	Simple version of weighted input image.
`private ModelSimpleImage`	`simpleWeightRSub16_1`	Simple version of weighted input image, subsampled by 16.
`private ModelSimpleImage`	`simpleWeightRSub2_1`	Simple version of weighted input image, subsampled by 2.
`private ModelSimpleImage`	`simpleWeightRSub4_1`	Simple version of weighted input image, subsampled by 4.
`private ModelSimpleImage`	`simpleWeightRSub8_1`	Simple version of weighted input image, subsampled by 8.
`private float[][]`	`trans`	DOCUMENT ME!
`private int[]`	`TransMatsInumber`
`private boolean`	`useOutsideReferenceVolume`	DOCUMENT ME!
`private TransMatrix[]`	`VolumesToReferenceTransformations`
`private boolean`	`weighted`	Flag to determine if there are weighted images or not.
`private ModelImage`	`weightVolumeImage`	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
`AlgorithmRegELSUNCOAR35D(ModelImage _image, int _costChoice, int _DOF, int _interp, int _interp2, int mode, int refImageNum, float _rotateBegin, float _rotateEnd, float _coarseRate, float _fineRate, boolean doGraph, boolean doSubsample, boolean fastMode, int _baseNumIter, int _numMinima, int searchAlgorithm)`	Creates new automatic internal registration algorithm and sets necessary variables.
`AlgorithmRegELSUNCOAR35D(ModelImage _image, ModelImage _inputWeight, int _costChoice, int _DOF, int _interp, int _interp2, int mode, int refImageNum, float _rotateBegin, float _rotateEnd, float _coarseRate, float _fineRate, boolean doGraph, boolean doSubsample, boolean fastMode, int _baseNumIter, int _numMinima, int searchAlgorithm)`	Creates new automatic internal registration algorithm and sets necessary variables.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`static WildMagic.LibFoundation.Mathematics.Vector3f`	`calculateCenterOfMass3D(ModelSimpleImage image, ModelSimpleImage wgtImage, boolean isColor)`	Calculates the center of mass (gravity) of a 3D image.
`private void`	`copyFloatData(ModelSimpleImage srcImage, ModelSimpleImage resultImage)`	DOCUMENT ME!
`void`	`disposeLocal()`	Dispose of local variables that may be taking up lots of room.
`void`	`finalize()`	Prepares this class for destruction.
`void`	`generateTestImages()`	A test of the diffeomorphic demons is to transform a circle image to a letter C image.
`double`	`getAnswer()`
`TransMatrix[]`	`getArrayTransMatrix()`
`private java.lang.String`	`getConstructionInfo()`	Creates a string with the parameters that the image was constructed with.
`float[][]`	`getRot()`	accessor for rot.
`private double[]`	`getTolerance(int DOF)`	Gets the tolerance vector based on the degrees of freedom (the length of the tolerance is the degrees of freedom) and the level of subsampling (1, 2, 4, 8).
`float[][]`	`getTrans()`	accessor for trans.
`TransMatrix`	`getTransform()`
`ModelImage`	`getTransformedImage()`	accessor to get the internally registered image.
`private void`	`interpolate(double x, double[] initial, double[][] tranforms, boolean scale)`	Performs a bilinear interpolation on points.
`private void`	`interpolate(double x, double y, double z, double[] initial, double[][][][] tranforms, boolean scale)`	Performs a trilinear interpolation on points.
`private java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem>[]`	`levelEight(ModelSimpleImage ref, ModelSimpleImage input)`	Takes two images that have been subsampled by a factor of eight.
`private java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem>`	`levelFour(ModelSimpleImage ref, ModelSimpleImage input, java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem> minima, java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem> optMinima)`	Takes two images that have been subsampled by a factor of four, and two vectors of minima.
`private AlgorithmRegELSUNCOAR35D.MatrixListItem`	`levelOne(ModelSimpleImage ref, ModelSimpleImage input, AlgorithmRegELSUNCOAR35D.MatrixListItem item, int maxIter)`	Takes the two images, no subsampling, and the best minimum so far.
`private AlgorithmRegELSUNCOAR35D.MatrixListItem`	`levelTwo(ModelSimpleImage ref, ModelSimpleImage input, java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem> minima)`	Takes two images that have been subsampled by a factor of 2 and a vector of minima.
`void`	`runAlgorithm()`	Runs the image registration.
`boolean`	`setReferenceVolume(ModelImage refVolume)`	allows the user to pass in an OUTSIDE reference volume.
`private void`	`subSampleBy2(ModelSimpleImage srcImage, ModelSimpleImage resultImage, boolean isColor)`	Takes a simple image and subsamples it by 2, interpolating so that the new values are averages.
`private static ModelSimpleImage`	`subSampleBy2XY(ModelSimpleImage srcImage, ModelSimpleImage resultImage, boolean isColor)`	Takes a simple image and subsamples XY by 2, interpolating so that the new XY values are averages.

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
  - ADJACENT
```
private static final int ADJACENT
```
    DOCUMENT ME!
    
    See Also:
    
    Constant Field Values
  - AVERAGE
```
private static final int AVERAGE
```
    DOCUMENT ME!
    
    See Also:
    
    Constant Field Values
  - minimumZForSub
```
private static final int minimumZForSub
```
    DOCUMENT ME!
    
    See Also:
    
    Constant Field Values
  - allowLevel16XY
```
private boolean allowLevel16XY
```
    DOCUMENT ME!
  - allowLevel16Z
```
private boolean allowLevel16Z
```
    DOCUMENT ME!
  - allowLevel2XY
```
private boolean allowLevel2XY
```
    Flags are true if weighted image is not present or if weighted image subsampling occurred, false if weighted image subsampling did not occur.
  - allowLevel2Z
```
private boolean allowLevel2Z
```
    DOCUMENT ME!
  - allowLevel4XY
```
private boolean allowLevel4XY
```
    DOCUMENT ME!
  - allowLevel4Z
```
private boolean allowLevel4Z
```
    DOCUMENT ME!
  - allowLevel8XY
```
private boolean allowLevel8XY
```
    DOCUMENT ME!
  - allowLevel8Z
```
private boolean allowLevel8Z
```
    DOCUMENT ME!
  - answer
```
private AlgorithmRegELSUNCOAR35D.MatrixListItem answer
```
    Final answer after registration.
  - buffer
```
private float[] buffer
```
    DOCUMENT ME!
  - bufferA
```
private float[] bufferA
```
    DOCUMENT ME!
  - bufferIW
```
private float[] bufferIW
```
    DOCUMENT ME!
  - bufferW
```
private float[] bufferW
```
    DOCUMENT ME!
  - calcCOG
```
private boolean calcCOG
```
    If true calculate the center of gravity (mass) and use the difference to intialize the translation. If false, images are pretty much aligned and don't calculate COG.
  - coarseNum
```
private int coarseNum
```
    Number of passes that will be made in the coarse sampling and fine sampling.
  - fineNum
```
private int fineNum
```
    Number of passes that will be made in the coarse sampling and fine sampling.
  - colorFactor
```
private int colorFactor
```
    1 for black and white, 4 for color.
  - costChoice
```
private int costChoice
```
    Choice of which cost function to use.
  - searchAlgorithm
```
private int searchAlgorithm
```
  - doColor
```
private boolean doColor
```
    DOCUMENT ME!
  - VolumesToReferenceTransformations
```
private TransMatrix[] VolumesToReferenceTransformations
```
  - DOF
```
private int DOF
```
    Maximum degrees of freedom when running the optimization.
  - doGraph
```
private boolean doGraph
```
    Produce 2 output graphs - 1 for 3 rotations and 1 for 3 translations. Only can be used for DOF == 6 and register to average or reference volume
  - doSubsample
```
private boolean doSubsample
```
    if true subsample.
  - fastMode
```
private boolean fastMode
```
    If true this algorithm skips all subsample and goes directly to the level 1 optimization. This assumes that images are fairly well aligned to begin with and therefore no sophisticated search is needed.
  - imageWeightIso
```
private ModelImage imageWeightIso
```
    DOCUMENT ME!
  - input1
```
private ModelImage input1
```
    Image used to import a volume from inputImage.
  - inputImage
```
private ModelImage inputImage
```
    This is the image in which internal registration will be performed.
  - inputw_1
```
private ModelImage inputw_1
```
    Image used to import a volume from inputWeight.
  - inputWeight
```
private ModelImage inputWeight
```
    This gives weights for the input image - higher weights mean a greater impact in that area on the registration.
  - interp
```
private int interp
```
    Interpolation method used in transformations.
  - interp2
```
private int interp2
```
    Interpolation method used in output.
  - iResols
```
private float[] iResols
```
    The voxel resolutions of the input image.
  - isoImage
```
private ModelImage isoImage
```
    new to this version: must keep isoImage around if image needed to be transformed.
  - level1FactorXY
```
private float level1FactorXY
```
    Multiplication factor for level 1 - will be set based on subsampling.
  - level1FactorZ
```
private float level1FactorZ
```
    DOCUMENT ME!
  - level2FactorXY
```
private float level2FactorXY
```
    Multiplication factor for level 2 - will be set based on subsampling.
  - level2FactorZ
```
private float level2FactorZ
```
    DOCUMENT ME!
  - level4FactorXY
```
private float level4FactorXY
```
    Multiplication factor for level 4 - will be set based on subsampling.
  - level4FactorZ
```
private float level4FactorZ
```
    DOCUMENT ME!
  - maxDim
```
private int maxDim
```
    DOCUMENT ME!
  - maxIter
```
private int maxIter
```
    Limits number of iterations in elsunc optimization. maxIter in the call to elsunc will be an integer multiple of baseNumIter
  - baseNumIter
```
private int baseNumIter
```
    Limits number of iterations in elsunc optimization. maxIter in the call to elsunc will be an integer multiple of baseNumIter
  - mode
```
private int mode
```
    DOCUMENT ME!
  - numMinima
```
private int numMinima
```
    Number of minima from level 8 to test at level 4.
  - output_1
```
private ModelImage output_1
```
    DOCUMENT ME!
  - outsidePreReferenceVolume
```
private ModelImage outsidePreReferenceVolume
```
    DOCUMENT ME!
  - outsideReferenceVolume
```
private ModelImage outsideReferenceVolume
```
    DOCUMENT ME!
  - refImageNo
```
private int refImageNo
```
    Indicates the first volume used as a reference volume, if mode == ADJACENT.
  - regToAdjImage
```
private boolean regToAdjImage
```
    If true use adjacent image for registration. If false use the average volume or use the image volume reference number to indicate the image volume to register all images to.
  - resample
```
private boolean resample
```
    true if resolutions unequal, false if resolutions equal.
  - resampleW
```
private boolean resampleW
```
    true if weight image is resampled.
  - rot
```
private float[][] rot
```
    Arrays used for producing graphs for DOF == 6 and register to average or reference image.
  - rotateBegin
```
private float rotateBegin
```
    Coarse and fine sampling parameters.
  - rotateEnd
```
private float rotateEnd
```
    Coarse and fine sampling parameters.
  - coarseRate
```
private float coarseRate
```
    Coarse and fine sampling parameters.
  - fineRate
```
private float fineRate
```
    Coarse and fine sampling parameters.
  - simpleInput_1
```
private ModelSimpleImage simpleInput_1
```
    Simple version of an image volume of the input image.
  - simpleInputSub16_1
```
private ModelSimpleImage simpleInputSub16_1
```
    Simple version of an image volume of the input image, subsampled by 16.
  - simpleInputSub2_1
```
private ModelSimpleImage simpleInputSub2_1
```
    Simple version of an image volume of the input image, subsampled by 2.
  - simpleInputSub4_1
```
private ModelSimpleImage simpleInputSub4_1
```
    Simple version of an image volume of the input image, subsampled by 4.
  - simpleInputSub8_1
```
private ModelSimpleImage simpleInputSub8_1
```
    Simple version of an image volume of the input image, subsampled by 8.
  - simpleRef_1
```
private ModelSimpleImage simpleRef_1
```
    Simple version of input image.
  - simpleRefSub16_1
```
private ModelSimpleImage simpleRefSub16_1
```
    Simple version of input image, subsampled by 16.
  - simpleRefSub2_1
```
private ModelSimpleImage simpleRefSub2_1
```
    Simple version of input image, subsampled by 2.
  - simpleRefSub4_1
```
private ModelSimpleImage simpleRefSub4_1
```
    Simple version of input image, subsampled by 4.
  - simpleRefSub8_1
```
private ModelSimpleImage simpleRefSub8_1
```
    Simple version of input image, subsampled by 8.
  - simpleWeightI_1
```
private ModelSimpleImage simpleWeightI_1
```
    DOCUMENT ME!
  - simpleWeightISub16_1
```
private ModelSimpleImage simpleWeightISub16_1
```
    DOCUMENT ME!
  - simpleWeightISub2_1
```
private ModelSimpleImage simpleWeightISub2_1
```
    DOCUMENT ME!
  - simpleWeightISub4_1
```
private ModelSimpleImage simpleWeightISub4_1
```
    DOCUMENT ME!
  - simpleWeightISub8_1
```
private ModelSimpleImage simpleWeightISub8_1
```
    DOCUMENT ME!
  - simpleWeightR_1
```
private ModelSimpleImage simpleWeightR_1
```
    Simple version of weighted input image.
  - simpleWeightRSub16_1
```
private ModelSimpleImage simpleWeightRSub16_1
```
    Simple version of weighted input image, subsampled by 16.
  - simpleWeightRSub2_1
```
private ModelSimpleImage simpleWeightRSub2_1
```
    Simple version of weighted input image, subsampled by 2.
  - simpleWeightRSub4_1
```
private ModelSimpleImage simpleWeightRSub4_1
```
    Simple version of weighted input image, subsampled by 4.
  - simpleWeightRSub8_1
```
private ModelSimpleImage simpleWeightRSub8_1
```
    Simple version of weighted input image, subsampled by 8.
  - trans
```
private float[][] trans
```
    DOCUMENT ME!
  - useOutsideReferenceVolume
```
private boolean useOutsideReferenceVolume
```
    DOCUMENT ME!
  - weighted
```
private boolean weighted
```
    Flag to determine if there are weighted images or not.
  - weightVolumeImage
```
private ModelImage weightVolumeImage
```
    DOCUMENT ME!
  - doMultiThread
```
private boolean doMultiThread
```
  - TransMatsInumber
```
private int[] TransMatsInumber
```
- Constructor Detail
  - AlgorithmRegELSUNCOAR35D
```
public AlgorithmRegELSUNCOAR35D(ModelImage _image,
                                int _costChoice,
                                int _DOF,
                                int _interp,
                                int _interp2,
                                int mode,
                                int refImageNum,
                                float _rotateBegin,
                                float _rotateEnd,
                                float _coarseRate,
                                float _fineRate,
                                boolean doGraph,
                                boolean doSubsample,
                                boolean fastMode,
                                int _baseNumIter,
                                int _numMinima,
                                int searchAlgorithm)
```
    Creates new automatic internal registration algorithm and sets necessary variables.
    
    Parameters:
    
    _image - Input image
    
    _costChoice - Choice of cost functions, like correlation ratio or mutual information.
    
    _DOF - Degrees of freedom for registration
    
    _interp - Interpolation method used in transformations.
    
    _interp2 - Interpolation method used in output
    
    mode - 1 = adjacent, 2 = average, 3 = reference
    
    refImageNum - If mode == REFERENCE, the volume all other volumes are registered to. If mode == ADJACENT, the first volume used as a reference.
    
    _rotateBegin - Beginning of coarse sampling range (i.e., -60 degrees).
    
    _rotateEnd - End of coarse sampling range (i.e., 60 degrees).
    
    _coarseRate - Point at which coarse samples should be taken (i.e., every 45 degrees).
    
    _fineRate - Point at which fine samples should be taken (i.e., every 15 degrees).
    
    doGraph - If true produce 2 output graphs - one for 3 rotations and one for 3 translations
    
    doSubsample - If true subsample
    
    fastMode - If true skip subsample and go directly to level 1 optimization
    
    _baseNumIter - Limits the number of iterations of elsunc algorithm. maxIter in the call to elsunc will be an integer multiple of baseNumIter
    
    _numMinima - Number of minima from level 8 to test at level 4
    
    searchAlgorithm - ELSUNC, NL2SOL, or LEVENBERG_MARQUARDT
  - AlgorithmRegELSUNCOAR35D
```
public AlgorithmRegELSUNCOAR35D(ModelImage _image,
                                ModelImage _inputWeight,
                                int _costChoice,
                                int _DOF,
                                int _interp,
                                int _interp2,
                                int mode,
                                int refImageNum,
                                float _rotateBegin,
                                float _rotateEnd,
                                float _coarseRate,
                                float _fineRate,
                                boolean doGraph,
                                boolean doSubsample,
                                boolean fastMode,
                                int _baseNumIter,
                                int _numMinima,
                                int searchAlgorithm)
```
    Creates new automatic internal registration algorithm and sets necessary variables.
    
    Parameters:
    
    _image - Input image
    
    _inputWeight - Input weighted image, used to give certain areas of the image greater impact on the registration.
    
    _costChoice - Choice of cost functions, like correlation ratio or mutual information.
    
    _DOF - DOCUMENT ME!
    
    _interp - Interpolation method used in transformations.
    
    _interp2 - Interpolation method used in output
    
    mode - 1 = adjacent, 2 = average, 3 = average
    
    refImageNum - If mode == REFERENCE, the volume all other volumes are registered to. If mode == ADJACENT, the first volume used as a reference.
    
    _rotateBegin - Beginning of coarse sampling range (i.e., -60 degrees).
    
    _rotateEnd - End of coarse sampling range (i.e., 60 degrees).
    
    _coarseRate - Point at which coarse samples should be taken (i.e., every 45 degrees).
    
    _fineRate - Point at which fine samples should be taken (i.e., every 15 degrees).
    
    doGraph - If true produce 2 output graphs - 1 for 3 rotations and one for 3 translations
    
    doSubsample - If true subsample
    
    fastMode - If true skip subsample and go directly to level 1 optimization
    
    _baseNumIter - Limits the number of iterations of elsunc algorithm. maxIter in the call to elsunc will be an integer multiple of baseNumIter
    
    _numMinima - Number of minima from level 8 to test at level 4
    
    searchAlgorithm - ELSUNC, NL2SOL, or LEVENBERG_MARQUARDT
- Method Detail
  - calculateCenterOfMass3D
```
public static WildMagic.LibFoundation.Mathematics.Vector3f calculateCenterOfMass3D(ModelSimpleImage image,
                                                                                   ModelSimpleImage wgtImage,
                                                                                   boolean isColor)
```
    Calculates the center of mass (gravity) of a 3D image. In image space where the upper left hand corner of the image is 0,0. The x axis goes left to right, y axis goes top to bottom and z axis goes into the screen. (i.e. the right hand rule). One could simply multiply by voxel resolutions.
    
    Parameters:
    
    image - the center of mass will be calculated from this image data
    
    wgtImage - DOCUMENT ME!
    
    isColor - DOCUMENT ME!
    
    Returns:
    
    the center of mass as a 3D point
  - disposeLocal
```
public void disposeLocal()
```
    Dispose of local variables that may be taking up lots of room.
  - finalize
```
public void finalize()
```
    Prepares this class for destruction.
    
    Overrides:
    
    finalize in class AlgorithmBase
  - getRot
```
public float[][] getRot()
```
    accessor for rot.
    
    Returns:
    
    rot
  - getTrans
```
public float[][] getTrans()
```
    accessor for trans.
    
    Returns:
    
    trans
  - getArrayTransMatrix
```
public TransMatrix[] getArrayTransMatrix()
```
  - getTransform
```
public TransMatrix getTransform()
```
  - getAnswer
```
public double getAnswer()
```
  - getTransformedImage
```
public ModelImage getTransformedImage()
```
    accessor to get the internally registered image.
    
    Returns:
    
    inputImage
  - runAlgorithm
```
public void runAlgorithm()
```
    Runs the image registration. If the resolutions are unequal, the image is transformed into isotropic pixels. The resolutions of the two images after the xyz isotropic transformation will be the same in the x, y, and z dimensions. That resolution will equal the minimum resolution. If the image is weighted, the weight image is transformed into isotropic pixels in the same manner as the original. Then the image is subsampled by 2, 4, and 8 or 16. If the image is too small it will not be subsampled down to the smallest level; if it is too big, it will be subsampled to 8 or 16. The same is done with the weight image if necessary. The program loops thru levelEight, levelFour, levelTwo, and levelOne with one volume as the reference volume and one volume as the input volume. The function levelEight is called with the volumes subsampled by 8 or 16; it returns two vectors with minima. Then the function levelFour is called with volumes subsampled by 4 and the two vectors; it returns one vector of minima. The function levelTwo is called with volumes subsampled by 2 and the vector; it returns an "answer" in the form of a MatrixListItem, which is a convenient way of storing the point, the matrix, and the cost of the minimum. Then the function levelOne is called with the minimum; it returns a final "answer", or minimum, which is used to register the input volume to the reference volume. The registered input volume data is imported into inputImage.
    
    Specified by:
    
    runAlgorithm in class AlgorithmBase
  - generateTestImages
```
public void generateTestImages()
```
    A test of the diffeomorphic demons is to transform a circle image to a letter C image.
  - setReferenceVolume
```
public boolean setReferenceVolume(ModelImage refVolume)
```
    allows the user to pass in an OUTSIDE reference volume.
    
    Parameters:
    
    refVolume - 3-Dim image for reference
    
    Returns:
    
    DOCUMENT ME!
  - subSampleBy2XY
```
private static ModelSimpleImage subSampleBy2XY(ModelSimpleImage srcImage,
                                               ModelSimpleImage resultImage,
                                               boolean isColor)
```
    Takes a simple image and subsamples XY by 2, interpolating so that the new XY values are averages.
    
    Parameters:
    
    srcImage - Image to subsample.
    
    resultImage - DOCUMENT ME!
    
    isColor - DOCUMENT ME!
    
    Returns:
    
    Subsampled image.
  - copyFloatData
```
private void copyFloatData(ModelSimpleImage srcImage,
                           ModelSimpleImage resultImage)
```
    DOCUMENT ME!
    
    Parameters:
    
    srcImage - DOCUMENT ME!
    
    resultImage - DOCUMENT ME!
  - getConstructionInfo
```
private java.lang.String getConstructionInfo()
```
    Creates a string with the parameters that the image was constructed with.
    
    Returns:
    
    Construction info.
  - getTolerance
```
private double[] getTolerance(int DOF)
```
    Gets the tolerance vector based on the degrees of freedom (the length of the tolerance is the degrees of freedom) and the level of subsampling (1, 2, 4, 8).
    
    Parameters:
    
    DOF - Degrees of freedom, will be length of vector.
    
    Returns:
    
    New tolerance vector to send to optimization.
    Based on FLIRT paper: let n=pixel dimension (in one dimension) R=brain radius, here assumed to be half of field-of-view Translation tolerance = n/2 Rotation tolerance = (180/PI)*n/(2R) (converted to degrees because AlgorithmELSUNC works in degrees) Scaling tolerance = n/(2R) Skewing tolerance = n/(2R)
  - interpolate
```
private void interpolate(double x,
                         double[] initial,
                         double[][] tranforms,
                         boolean scale)
```
    Performs a bilinear interpolation on points. Takes an initial point, a vector of values to set, and an array in which to look at neighbors of that point. Sets the appropriate values in the vector. Does not set scale if the scale parameter is false.
    
    Parameters:
    
    x - Initial index into array.
    
    initial - Vector to set; if scale is true, set two translations and a scale. Otherwise just set translations.
    
    tranforms - DOCUMENT ME!
    
    scale - true means set the scale in the vector.
  - interpolate
```
private void interpolate(double x,
                         double y,
                         double z,
                         double[] initial,
                         double[][][][] tranforms,
                         boolean scale)
```
    Performs a trilinear interpolation on points. Takes 3 initial points, a vector of values to set, and an array in which to look at neighbors of those points. Sets the appropriate values in the vector. Does not set scale if the scale parameter is false.
    
    Parameters:
    
    x - X rotation initial index into array.
    
    y - Y rotation initial index into array.
    
    z - Z rotation initial index into array.
    
    initial - Vector to set; if scale is true, set three translations and a scale. Otherwise just set translations.
    
    tranforms - DOCUMENT ME!
    
    scale - true means set the scale in the vector.
  - levelEight
```
private java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem>[] levelEight(ModelSimpleImage ref,
                                                                               ModelSimpleImage input)
```
    Takes two images that have been subsampled by a factor of eight. Sets up the cost function with the images and the weighted images, if necessary. Uses the coarse sampling rate and optimizes translations and global scale at the given rotation. So for example, if the coarse sampling range were -30 to 30 at every 15 degrees, we would optimize at rotations of (-30, -30, -30), (-30, -30, -15), (-30, -30, 0), etc. In this case there would be a total of 125 calls to the optimization method. Measures the cost at the fine sampling rate. Interpolates the translations and global scale to come up with a good guess as to what the optimized translation would be at that point. Takes the top 20% of the points and optimizes them. Now have a large multi-array of costs. 20% of those have been optimized and placed back into their original position in the multi-array. Looks at the 8 neighbors of a point: +, =, or - one fine sample in each of the three directions. If the point has a cost greater than any of these, it is not a minima. Otherwise it is. Saves it in a vector of minima. Optimizes the minima over rotations as well as translations and global scale. (Previously had not optimized over rotations.) Returns two vectors, one containing the minima before optimization, one containing the minima after optimization.
    
    Parameters:
    
    ref - Subsampled by 8 reference image.
    
    input - Subsampled by 8 input image.
    
    Returns:
    
    List of preoptimized and optimized points.
  - levelFour
```
private java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem> levelFour(ModelSimpleImage ref,
                                                                            ModelSimpleImage input,
                                                                            java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem> minima,
                                                                            java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem> optMinima)
```
    Takes two images that have been subsampled by a factor of four, and two vectors of minima. Sets up the cost function with the images and the weighted images, if necessary. Adds the level4Factor determined during subsampling. Measures the costs of the minima on the images and sort them. Takes the top three in each vector (pre-optimization and post-optimization) and optimizes them. Puts them all into one vector. Perturbs the rotations in each dimension by zero and plus-minus fineDelta. If it's not a rigid transformation, perturbs the scales by factors of 0.8, 0.9, 1.0, 1.1, and 1.2. Optimize the perturbations. Returns a vector of the perturbed, optimized minima.
    
    Parameters:
    
    ref - Reference image, subsampled by 4.
    
    input - Input image, subsampled by 4.
    
    minima - Preoptimized minima.
    
    optMinima - Optimized minima.
    
    Returns:
    
    A vector of perturbed, optimized minima.
  - levelOne
```
private AlgorithmRegELSUNCOAR35D.MatrixListItem levelOne(ModelSimpleImage ref,
                                                         ModelSimpleImage input,
                                                         AlgorithmRegELSUNCOAR35D.MatrixListItem item,
                                                         int maxIter)
```
    Takes the two images, no subsampling, and the best minimum so far. Sets up the cost function with the images and the weighted images, if necessary. Adds the level1Factor determined during subsampling. Performs one optimization run, with the maximum allowable degrees of freedom as specified by the user (the max is 12). Returns the best minimum.
    
    Parameters:
    
    ref - Reference image.
    
    input - Input image.
    
    item - Best minimum so far.
    
    maxIter - DOCUMENT ME!
    
    Returns:
    
    Best minimum after optimization.
  - levelTwo
```
private AlgorithmRegELSUNCOAR35D.MatrixListItem levelTwo(ModelSimpleImage ref,
                                                         ModelSimpleImage input,
                                                         java.util.Vector<AlgorithmRegELSUNCOAR35D.MatrixListItem> minima)
```
    Takes two images that have been subsampled by a factor of 2 and a vector of minima. Sets up the cost function with the images and the weighted images, if necessary. Adds the level2Factor determined during subsampling. Measures the costs of the minima at the images. Optimizes the best minimum with 7 degrees of freedom, then 9, then 12. If the user has limited the degrees of freedom to 6, there will only be one optimization run, with 6 degrees of freedom. Returns the best minimum after optimization.
    
    Parameters:
    
    ref - Reference image, subsampled by 2.
    
    input - Input image, subsampled by 2.
    
    minima - Minima.
    
    Returns:
    
    The optimized minimum.
  - subSampleBy2
```
private void subSampleBy2(ModelSimpleImage srcImage,
                          ModelSimpleImage resultImage,
                          boolean isColor)
```
    Takes a simple image and subsamples it by 2, interpolating so that the new values are averages.
    
    Parameters:
    
    srcImage - Image to subsample.
    
    resultImage - DOCUMENT ME!
    
    isColor - DOCUMENT ME!

Class AlgorithmRegELSUNCOAR35D

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

ADJACENT

AVERAGE

minimumZForSub

allowLevel16XY

allowLevel16Z

allowLevel2XY

allowLevel2Z

allowLevel4XY

allowLevel4Z

allowLevel8XY

allowLevel8Z

answer

buffer

bufferA

bufferIW

bufferW

calcCOG

coarseNum

fineNum

colorFactor

costChoice

searchAlgorithm

doColor

VolumesToReferenceTransformations

DOF

doGraph

doSubsample

fastMode

imageWeightIso

input1

inputImage

inputw_1

inputWeight

interp

interp2

iResols

isoImage

level1FactorXY

level1FactorZ

level2FactorXY

level2FactorZ

level4FactorXY

level4FactorZ

maxDim

maxIter

baseNumIter

mode

numMinima

output_1

outsidePreReferenceVolume

outsideReferenceVolume

refImageNo

regToAdjImage

resample

resampleW

rot

rotateBegin

rotateEnd

coarseRate

fineRate

simpleInput_1

simpleInputSub16_1

simpleInputSub2_1

simpleInputSub4_1

simpleInputSub8_1

simpleRef_1

simpleRefSub16_1

simpleRefSub2_1