Package gov.nih.mipav.model.algorithms.registration

Class AlgorithmRegELSUNCOAR3D

java.lang.Object
java.lang.Thread
gov.nih.mipav.model.algorithms.AlgorithmBase
gov.nih.mipav.model.algorithms.registration.AlgorithmRegELSUNCOAR3D
All Implemented Interfaces:
AlgorithmInterface, ActionListener, WindowListener, Runnable, EventListener
public class AlgorithmRegELSUNCOAR3D extends AlgorithmBase implements AlgorithmInterface
This is an automatic registration method based on FLIRT. FLIRT stands for FMRIB's Linear Image Registration Tool 1.3. 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.

Our algorithm works as follows:
1.) We find the minimum resolution of the images and blur them if neccessary.
2.) We transform the images into isotropic voxels.
3.) We subsample the images by 2, 4, and 8, depending on the resolution.
Subsampling can be performed for x, y, and z or for only x and y. 4.) With the images that were subsampled by 8, 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, -30, -30), (-30, -30, -15), (-30, -30, 0), etc. In this case there would be a total of 125 calls to the optimization method.
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 in each of the three directions. 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 rotations as well as translations and global scale. (Previously we had not optimized over rotations.) We return two vectors, one containing the minima before optimization, one containing the minima after optimization.
9.) We now call levelFour with the images subsampled by 4 and the vectors of minima. We measure the costs of the minima on the new images 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 rotations in each dimension by zero and plus-minus fineDelta. If it's not a rigid transformation, we then perturb the 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 images subsampled by 2. We measure the costs of the minima at the new images. 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 images. 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 matrix from this answer is saved in a file and also accessed by the dialog that called this algorithm.

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

Author:
Neva Cherniavsky, Matthew McAuliffe

  • Field Details

    • minimumZForSub

      private static final int minimumZForSub
      DOCUMENT ME!
      See Also:

    • initialCost

      double initialCost
      DOCUMENT ME!

    • timeElapsed

      long timeElapsed
      DOCUMENT ME!

    • timeLater

      long timeLater
      DOCUMENT ME!

    • timeNow

      long timeNow
      DOCUMENT ME!

    • tMatrix

      TransMatrix tMatrix
      DOCUMENT ME!

    • allowLevel16XY

      private boolean allowLevel16XY
      DOCUMENT ME!

    • allowLevel16Z

      private boolean allowLevel16Z
      DOCUMENT ME!

    • allowLevel2XY

      private boolean allowLevel2XY
      DOCUMENT ME!

    • 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 MatrixListItem answer
      Final answer after registration.

    • bestGuessLevel2

      private MatrixListItem bestGuessLevel2
      DOCUMENT ME!

    • blurredInput

      private ModelImage blurredInput
      Blurred input image.

    • blurredRef

      private ModelImage blurredRef
      Blurred reference image.

    • calcCOG

      private final 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 then don't calculated COG.
      See Also:

    • coarseNumX

      private final int coarseNumX
      Number of passes that will be made in the coarse sampling and fine sampling.

    • fineNumX

      private final int fineNumX
      Number of passes that will be made in the coarse sampling and fine sampling.

    • coarseNumY

      private final int coarseNumY
      DOCUMENT ME!

    • fineNumY

      private final int fineNumY
      DOCUMENT ME!

    • coarseNumZ

      private final int coarseNumZ
      DOCUMENT ME!

    • fineNumZ

      private final int fineNumZ
      DOCUMENT ME!

    • costChoice

      private int costChoice
      Choice of which cost function to use.

    • doColor

      private boolean doColor
      DOCUMENT ME!

    • DOF

      private final int DOF
      Maximum degrees of freedom when running the optimization.

    • doSubsample

      private boolean doSubsample
      If true subsample for levelEight, levelFour and levelTwo analyses.

    • doMultiThread

      private boolean doMultiThread

    • 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.

    • fullAnalysisMode

      private boolean fullAnalysisMode
      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.

    • imageInputIso

      private ModelImage imageInputIso
      Isotropic input image.

    • imageRefIso

      private ModelImage imageRefIso
      Isotropic reference image.

    • imageWeightInputIso

      private ModelImage imageWeightInputIso
      Isotropic weighted input image.

    • imageWeightRefIso

      private ModelImage imageWeightRefIso
      Isotropic weighted reference image.

    • inputImage

      private ModelImage inputImage
      This image is to registered to the reference image.

    • 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 final int interp
      Interpolation method.

    • 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
      Advanced optimization settings maxIter in the call to ELSUNC will be an integer multiple of baseNumIter.

    • baseNumIter

      final int baseNumIter

    • searchAlgorithm

      private int searchAlgorithm

    • maxResol

      private final boolean maxResol
      Flag to determine if the maximum of the minimum resolutions of the two datasets should be used. If true use the maximum resolution of the two dataset. Throws away information some image information but is faster. If false the algorithms uses the minimum of the resolutions when resampling the images. Can be slower but does not "lose" informaton.

    • numMinima

      private final int numMinima
      Number of minima from level 8 to test at level 4.

    • refImage

      private ModelImage refImage
      The inputImage will be registered to this reference image.

    • refWeight

      private ModelImage refWeight
      This gives weights for the reference image - higher weights mean a greater impact in that area on the registration.

    • resampleInput

      private boolean resampleInput
      DOCUMENT ME!

    • resampleRef

      private boolean resampleRef
      DOCUMENT ME!

    • resInput

      private float[] resInput
      The voxel resolutions of the image to be registered to the reference image.

    • resRef

      private float[] resRef
      The voxel resolutions of the reference image.

    • rotateBeginX

      private final float rotateBeginX
      Coarse and fine sampling parameters.

    • rotateEndX

      private final float rotateEndX
      Coarse and fine sampling parameters.

    • coarseRateX

      private final float coarseRateX
      Coarse and fine sampling parameters.

    • fineRateX

      private final float fineRateX
      Coarse and fine sampling parameters.

    • rotateBeginY

      private final float rotateBeginY
      DOCUMENT ME!

    • rotateEndY

      private final float rotateEndY
      DOCUMENT ME!

    • coarseRateY

      private final float coarseRateY
      DOCUMENT ME!

    • fineRateY

      private final float fineRateY
      DOCUMENT ME!

    • rotateBeginZ

      private final float rotateBeginZ
      DOCUMENT ME!

    • rotateEndZ

      private final float rotateEndZ
      DOCUMENT ME!

    • coarseRateZ

      private final float coarseRateZ
      DOCUMENT ME!

    • fineRateZ

      private final float fineRateZ
      DOCUMENT ME!

    • simpleInput

      private ModelSimpleImage simpleInput
      Simple version of input image.

    • simpleInputSub2

      private ModelSimpleImage simpleInputSub2
      Simple version of input image, subsampled by 2.

    • simpleInputSub4

      private ModelSimpleImage simpleInputSub4
      Simple version of input image, subsampled by 4.

    • simpleInputSub8

      private ModelSimpleImage simpleInputSub8
      Simple version of input image, subsampled by 8.

    • simpleRef

      private ModelSimpleImage simpleRef
      Simple version of reference image.

    • simpleRefSub2

      private ModelSimpleImage simpleRefSub2
      Simple version of reference image, subsampled by 2.

    • simpleRefSub4

      private ModelSimpleImage simpleRefSub4
      Simple version of reference image, subsampled by 4.

    • simpleRefSub8

      private ModelSimpleImage simpleRefSub8
      Simple version of reference image, subsampled by 8.

    • simpleWeightInput

      private ModelSimpleImage simpleWeightInput
      Simple version of weighted input image.

    • simpleWeightInputSub2

      private ModelSimpleImage simpleWeightInputSub2
      Simple version of weighted input image, subsampled by 2.

    • simpleWeightInputSub4

      private ModelSimpleImage simpleWeightInputSub4
      Simple version of weighted input image, subsampled by 4.

    • simpleWeightInputSub8

      private ModelSimpleImage simpleWeightInputSub8
      Simple version of weighted input image, subsampled by 8.

    • simpleWeightRef

      private ModelSimpleImage simpleWeightRef
      Simple version of weighted reference image.

    • simpleWeightRefSub2

      private ModelSimpleImage simpleWeightRefSub2
      Simple version of weighted reference image, subsampled by 2.

    • simpleWeightRefSub4

      private ModelSimpleImage simpleWeightRefSub4
      Simple version of weighted reference image, subsampled by 4.

    • simpleWeightRefSub8

      private ModelSimpleImage simpleWeightRefSub8
      Simple version of weighted reference image, subsampled by 8.

    • transform

      private AlgorithmTransform transform
      Transformation algorithm for creating an isotropic reference image.

    • transform2

      private AlgorithmTransform transform2
      Transformation algorithm for creating an isotropic input image.

    • weighted

      private boolean weighted
      Flag to determine if there are weighted images or not.

    • weightedInputPixels

      private int weightedInputPixels
      DOCUMENT ME!

    • weightedInputPixelsSub2

      private int weightedInputPixelsSub2
      DOCUMENT ME!

    • weightedInputPixelsSub4

      private int weightedInputPixelsSub4
      DOCUMENT ME!

    • weightedInputPixelsSub8

      private int weightedInputPixelsSub8
      DOCUMENT ME!

    • weightedRefPixels

      private int weightedRefPixels
      DOCUMENT ME!

    • weightedRefPixelsSub2

      private int weightedRefPixelsSub2
      DOCUMENT ME!

    • weightedRefPixelsSub4

      private int weightedRefPixelsSub4
      DOCUMENT ME!

    • weightedRefPixelsSub8

      private int weightedRefPixelsSub8
      DOCUMENT ME!

    • m_kGPUCost

      private ImageRegistrationGPU m_kGPUCost

    • doneSignal

      private CountDownLatch doneSignal

  • Constructor Details

    • AlgorithmRegELSUNCOAR3D

      public AlgorithmRegELSUNCOAR3D(ModelImage _imageA, ModelImage _imageB, int _costChoice, int _DOF, int _interp, float _rotateBeginX, float _rotateEndX, float _coarseRateX, float _fineRateX, float _rotateBeginY, float _rotateEndY, float _coarseRateY, float _fineRateY, float _rotateBeginZ, float _rotateEndZ, float _coarseRateZ, float _fineRateZ, boolean _maxResol, boolean _doSubsample, boolean _fastMode, int _baseNumIter, int _numMinima, int searchAlgorithm)
      Creates new automatic linear registration algorithm and sets necessary variables.
      Parameters:
      _imageA - Reference image (register input image to reference image).
      _imageB - Input image (register input image to reference image).
      _costChoice - Choice of cost functions, like correlation ratio or mutual information.
      _DOF - Degrees of freedom for registration
      _interp - Interpolation method used in transformations.
      _rotateBeginX - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndX - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateX - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateX - Point at which fine samples should be taken (i.e., every 15 degrees).
      _rotateBeginY - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndY - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateY - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateY - Point at which fine samples should be taken (i.e., every 15 degrees).
      _rotateBeginZ - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndZ - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateZ - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateZ - Point at which fine samples should be taken (i.e., every 15 degrees).
      _maxResol - If true is the maximum of the minimum resolution of the two datasets when resampling.
      _doSubsample - If true then subsample
      _fastMode - If true then searching the parameter space is not conducted and the algorithm proceeds to level one immediately
      _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 - ESLUNC, LEVENBERG_MARQUARDT, or NL2SOL.

    • AlgorithmRegELSUNCOAR3D

      public AlgorithmRegELSUNCOAR3D(ModelImage _imageA, ModelImage _imageB, int _costChoice, int _DOF, int _interp, float _rotateBeginX, float _rotateEndX, float _coarseRateX, float _fineRateX, float _rotateBeginY, float _rotateEndY, float _coarseRateY, float _fineRateY, float _rotateBeginZ, float _rotateEndZ, float _coarseRateZ, float _fineRateZ, boolean _maxResol, boolean _doSubsample, boolean _doMultiThread, boolean _fastMode, int _baseNumIter, int _numMinima, int searchAlgorithm)
      Creates new automatic linear registration algorithm and sets necessary variables.
      Parameters:
      _imageA - Reference image (register input image to reference image).
      _imageB - Input image (register input image to reference image).
      _costChoice - Choice of cost functions, like correlation ratio or mutual information.
      _DOF - Degrees of freedom for registration
      _interp - Interpolation method used in transformations.
      _rotateBeginX - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndX - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateX - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateX - Point at which fine samples should be taken (i.e., every 15 degrees).
      _rotateBeginY - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndY - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateY - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateY - Point at which fine samples should be taken (i.e., every 15 degrees).
      _rotateBeginZ - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndZ - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateZ - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateZ - Point at which fine samples should be taken (i.e., every 15 degrees).
      _maxResol - If true is the maximum of the minimum resolution of the two datasets when resampling.
      _doSubsample - If true then subsample
      _doMultiThread -
      _fastMode - If true then searching the parameter space is not conducted and the algorithm proceeds to level one immediately
      _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 - ESLUNC, LEVENBERG_MARQUARDT, or NL2SOL.

    • AlgorithmRegELSUNCOAR3D

      public AlgorithmRegELSUNCOAR3D(ModelImage _imageA, ModelImage _imageB, ModelImage _refWeight, ModelImage _inputWeight, int _costChoice, int _DOF, int _interp, float _rotateBeginX, float _rotateEndX, float _coarseRateX, float _fineRateX, float _rotateBeginY, float _rotateEndY, float _coarseRateY, float _fineRateY, float _rotateBeginZ, float _rotateEndZ, float _coarseRateZ, float _fineRateZ, boolean _maxResol, boolean _doSubsample, boolean _fastMode, int _baseNumIter, int _numMinima, int searchAlgorithm)
      Creates new automatic linear registration algorithm and sets necessary variables.
      Parameters:
      _imageA - Reference image (register input image to reference image).
      _imageB - Input image (register input image to reference image).
      _refWeight - Reference weighted image, used to give certain areas of the image greater impact on the registration.
      _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 - Degrees of freedom for registration
      _interp - Interpolation method used in transformations.
      _rotateBeginX - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndX - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateX - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateX - Point at which fine samples should be taken (i.e., every 15 degrees).
      _rotateBeginY - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndY - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateY - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateY - Point at which fine samples should be taken (i.e., every 15 degrees).
      _rotateBeginZ - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndZ - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateZ - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateZ - Point at which fine samples should be taken (i.e., every 15 degrees).
      _maxResol - If true is the maximum of the minimum resolution of the two datasets when resampling.
      _doSubsample - If true then subsample
      _fastMode - If true then searching the parameter space is not conducted and the algorithm proceeds to level one immediately
      _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 - ESLUNC, LEVENBERG_MARQUARDT, or NL2SOL.

    • AlgorithmRegELSUNCOAR3D

      public AlgorithmRegELSUNCOAR3D(ModelImage _imageA, ModelImage _imageB, ModelImage _refWeight, ModelImage _inputWeight, int _costChoice, int _DOF, int _interp, float _rotateBeginX, float _rotateEndX, float _coarseRateX, float _fineRateX, float _rotateBeginY, float _rotateEndY, float _coarseRateY, float _fineRateY, float _rotateBeginZ, float _rotateEndZ, float _coarseRateZ, float _fineRateZ, boolean _maxResol, boolean _doSubsample, boolean _doMultiThread, boolean _fastMode, int _baseNumIter, int _numMinima, int searchAlgorithm)
      Creates new automatic linear registration algorithm and sets necessary variables.
      Parameters:
      _imageA - Reference image (register input image to reference image).
      _imageB - Input image (register input image to reference image).
      _refWeight - Reference weighted image, used to give certain areas of the image greater impact on the registration.
      _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 - Degrees of freedom for registration
      _interp - Interpolation method used in transformations.
      _rotateBeginX - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndX - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateX - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateX - Point at which fine samples should be taken (i.e., every 15 degrees).
      _rotateBeginY - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndY - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateY - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateY - Point at which fine samples should be taken (i.e., every 15 degrees).
      _rotateBeginZ - Beginning of coarse sampling range (i.e., -60 degrees).
      _rotateEndZ - End of coarse sampling range (i.e., 60 degrees).
      _coarseRateZ - Point at which coarse samples should be taken (i.e., every 45 degrees).
      _fineRateZ - Point at which fine samples should be taken (i.e., every 15 degrees).
      _maxResol - If true is the maximum of the minimum resolution of the two datasets when resampling.
      _doSubsample - If true then subsample
      _doMultiThread -
      _fastMode - If true then searching the parameter space is not conducted and the algorithm proceeds to level one immediately
      _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 - ESLUNC, LEVENBERG_MARQUARDT, or NL2SOL.

  • Method Details

    • 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

    • getAnswer

      public double getAnswer()
      Accessor that returns the final cost function.
      Returns:
      Matrix found at the end of algorithm.

    • getTransArray

      public double[] getTransArray()
      Access that returns an array containing the transformation parameters.
      Returns:
      transformation array (0-2 rot, 3-5 trans, 6-9 scale, 9-12 skew)

    • getTransform

      public TransMatrix getTransform()
      Accessor that returns the matrix calculated in this algorithm.
      Returns:
      Matrix found at the end of algorithm.

    • getTransformHalf

      public TransMatrix getTransformHalf()
      Accessor that returns the matrix calculated in this algorithm divided by 2.
      Returns:
      Matrix found at the end of algorithm with the compoents halved.

    • getTransformMigsagittal

      public TransMatrix getTransformMigsagittal()
      Accessor that returns the z rot and x and y trans from the matrix calculated in this algorithm.
      Returns:
      z rotation and x and y translations from the matrix found at the end of algorithm.

    • runAlgorithm

      public void runAlgorithm()
      Runs the image registration. Blurs the images based on what their minimum resolutions are. The reference image is blurred if one of the input image resolutions is 50% or more bigger than the corresponding resolution in the reference image; likewise, the input image is blurred if one of the reference resolutions is 50% or more bigger than the corresponding resolution in the input image. Thus, it is unlikely, though not impossible, that both images will be blurred. The images are then transformed into isotropic voxels. The resolutions of the two images after the isotropic transformation will be the same in all dimensions. If maxResol is true, that resolution will equal the maximum of the minimums of each image's resolutions: Max( Min (resolutions of ref image, resolutions of input image) ). If the images are weighted, the weight images are blurred and transformed into isotropic voxels in the same manner as the originals. Then the images are subsampled by 2, 4, and 8. If the images are too small they will not be subsampled down to the smallest level; if they are too big, they will be subsampled to 16. The same is done with the weight images if necessary. The function levelEight is called with the images subsampled by 8; it returns two vectors with minima. Then the function levelFour is called with images subsampled by 4 and the two vectors; it returns one vector of minima. The function levelTwo is called with images 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 will then be accessed by the dialog that called this algorithm.
      Specified by:
      runAlgorithm in class AlgorithmBase

    • subsampleBy2

      public static ModelSimpleImage subsampleBy2(ModelSimpleImage srcImage, boolean isColor)
      Takes a simple image and subsamples it by 2, interpolating so that the new values are averages.
      Parameters:
      srcImage - Image to subsample.
      isColor - DOCUMENT ME!
      Returns:
      Subsampled image.

    • subsampleBy2XY

      public static ModelSimpleImage subsampleBy2XY(ModelSimpleImage srcImage, 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.
      isColor - DOCUMENT ME!
      Returns:
      Subsampled image.

    • getConstructionInfo

      private 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 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

      public Vector<MatrixListItem>[] levelEight(ModelSimpleImage ref, ModelSimpleImage input, float progressFrom, float progressTo)
      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. Removes those items that are outside the rotation begin and end limits. 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

      public Vector<MatrixListItem> levelFour(ModelSimpleImage ref, ModelSimpleImage input, Vector<MatrixListItem> minima, Vector<MatrixListItem> optMinima, float progressFrom, float progressTo)
      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

      public MatrixListItem levelOne(ModelSimpleImage ref, ModelSimpleImage input, MatrixListItem item, int maxIter, float progressFrom, float progressTo)
      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

      public MatrixListItem levelTwo(ModelSimpleImage ref, ModelSimpleImage input, Vector<MatrixListItem> minima, float progressFrom, float progressTo)
      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.

    • getLevel1FactorXY

      public float getLevel1FactorXY()

    • setLevel1FactorXY

      public void setLevel1FactorXY(float level1FactorXY)

    • getLevel1FactorZ

      public float getLevel1FactorZ()

    • setLevel1FactorZ

      public void setLevel1FactorZ(float level1FactorZ)

    • getLevel2FactorXY

      public float getLevel2FactorXY()

    • setLevel2FactorXY

      public void setLevel2FactorXY(float level2FactorXY)

    • getLevel2FactorZ

      public float getLevel2FactorZ()

    • setLevel2FactorZ

      public void setLevel2FactorZ(float level2FactorZ)

    • getLevel4FactorXY

      public float getLevel4FactorXY()

    • setLevel4FactorXY

      public void setLevel4FactorXY(float level4FactorXY)

    • getLevel4FactorZ

      public float getLevel4FactorZ()

    • setLevel4FactorZ

      public void setLevel4FactorZ(float level4FactorZ)

    • algorithmPerformed

      public void algorithmPerformed(AlgorithmBase algorithm)
      Description copied from interface: AlgorithmInterface
      Called after an algorithm this listener is registered to exits (maybe successfully, maybe not). If the algorithm is run in a separate thread, this call will be made within that thread. If not, this call will be made from that same, shared thread.
      Specified by:
      algorithmPerformed in interface AlgorithmInterface
      Parameters:
      algorithm - the algorithm which has just completed