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

Class AlgorithmConstrainedELSUNCOAR3D

  • All Implemented Interfaces:
    java.awt.event.ActionListener, java.awt.event.WindowListener, java.lang.Runnable, java.util.EventListener
    public class AlgorithmConstrainedELSUNCOAR3D
extends AlgorithmBase
    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 Detail

      • initialCost

        double initialCost
        DOCUMENT ME!

      • maxPossibleCost

        double maxPossibleCost
        DOCUMENT ME!

      • timeElapsed

        long timeElapsed
        DOCUMENT ME!

      • timeLater

        long timeLater
        DOCUMENT ME!

      • timeNow

        long timeNow
        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!

      • blurredInput

        private ModelImage blurredInput
        Blurred input image.

      • blurredRef

        private ModelImage blurredRef
        Blurred reference image.

      • bracketBound

        private int bracketBound
        Sets minimum and maximum limits as initial guess -+ unit_tolerance[i]*bracketBound.

      • 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 then don't calculated COG.

      • coarseNumX

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

      • fineNumX

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

      • coarseNumY

        private int coarseNumY
        DOCUMENT ME!

      • fineNumY

        private int fineNumY
        DOCUMENT ME!

      • coarseNumZ

        private int coarseNumZ
        DOCUMENT ME!

      • fineNumZ

        private int fineNumZ
        DOCUMENT ME!

      • costChoice

        private int costChoice
        Choice of which cost function to use.

      • doColor

        private boolean doColor
        DOCUMENT ME!

      • DOF

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

      • doSubsample

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

      • dummy

        private double[] dummy
        Dummy initial values used to create a ELSUNC algorithm instance before setting initial.

      • 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.

      • initialMessage

        private java.lang.String initialMessage
        DOCUMENT ME!

      • 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 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!

      • limits

        private float[][] limits
        Translation limits.

      • limits_mm

        private float[][] limits_mm
        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

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

      • maxResol

        private 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 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 float rotateBeginX
        Coarse and fine sampling parameters.

      • rotateRangeX

        private float rotateRangeX
        Coarse and fine sampling parameters.

      • coarseRateX

        private float coarseRateX
        Coarse and fine sampling parameters.

      • fineRateX

        private float fineRateX
        Coarse and fine sampling parameters.

      • rotateBeginY

        private float rotateBeginY
        DOCUMENT ME!

      • rotateRangeY

        private float rotateRangeY
        DOCUMENT ME!

      • coarseRateY

        private float coarseRateY
        DOCUMENT ME!

      • fineRateY

        private float fineRateY
        DOCUMENT ME!

      • rotateBeginZ

        private float rotateBeginZ
        DOCUMENT ME!

      • rotateRangeZ

        private float rotateRangeZ
        DOCUMENT ME!

      • coarseRateZ

        private float coarseRateZ
        DOCUMENT ME!

      • fineRateZ

        private 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!

    • Constructor Detail

      • AlgorithmConstrainedELSUNCOAR3D

        public AlgorithmConstrainedELSUNCOAR3D​(ModelImage _imageA,
                                       ModelImage _imageB,
                                       int _costChoice,
                                       int _DOF,
                                       int _interp,
                                       float _rotateBeginX,
                                       float _rotateRangeX,
                                       float _rotateBeginY,
                                       float _rotateRangeY,
                                       float _rotateBeginZ,
                                       float _rotateRangeZ,
                                       int _nCoarseX,
                                       int _nCoarseY,
                                       int _nCoarseZ,
                                       float[][] _transLimits,
                                       boolean _maxResol,
                                       boolean _doSubsample,
                                       boolean _fastMode,
                                       boolean _calcCOG,
                                       int _bracketBound,
                                       int _baseNumIter,
                                       int _numMinima)
        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).
        _rotateRangeX - DOCUMENT ME!
        _rotateBeginY - Beginning of coarse sampling range (i.e., -60 degrees).
        _rotateRangeY - DOCUMENT ME!
        _rotateBeginZ - Beginning of coarse sampling range (i.e., -60 degrees).
        _rotateRangeZ - DOCUMENT ME!
        _nCoarseX - DOCUMENT ME!
        _nCoarseY - DOCUMENT ME!
        _nCoarseZ - DOCUMENT ME!
        _transLimits - Limitations on translations.
        _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
        _calcCOG - If true calculate the center of gravity (mass) to initialize registration.
        _bracketBound - Sets minimum and maximum limits as initial guess -+ unit_tolerance[i]*bracketBound.
        _baseNumIter - Limits the number of iterations of Powell's algorithm. maxIter in the call to Powell's will be an integer multiple of baseNumIter
        _numMinima - Number of minima from level 8 to test at level 4

      • AlgorithmConstrainedELSUNCOAR3D

        public AlgorithmConstrainedELSUNCOAR3D​(ModelImage _imageA,
                                       ModelImage _imageB,
                                       ModelImage _refWeight,
                                       ModelImage _inputWeight,
                                       int _costChoice,
                                       int _DOF,
                                       int _interp,
                                       float _rotateBeginX,
                                       float _rotateRangeX,
                                       float _rotateBeginY,
                                       float _rotateRangeY,
                                       float _rotateBeginZ,
                                       float _rotateRangeZ,
                                       int _nCoarseX,
                                       int _nCoarseY,
                                       int _nCoarseZ,
                                       float[][] _transLimits,
                                       boolean _maxResol,
                                       boolean _doSubsample,
                                       boolean _fastMode,
                                       boolean _calcCOG,
                                       int _bracketBound,
                                       int _baseNumIter,
                                       int _numMinima)
        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).
        _rotateRangeX - DOCUMENT ME!
        _rotateBeginY - Beginning of coarse sampling range (i.e., -60 degrees).
        _rotateRangeY - DOCUMENT ME!
        _rotateBeginZ - Beginning of coarse sampling range (i.e., -60 degrees).
        _rotateRangeZ - DOCUMENT ME!
        _nCoarseX - DOCUMENT ME!
        _nCoarseY - DOCUMENT ME!
        _nCoarseZ - DOCUMENT ME!
        _transLimits - DOCUMENT ME!
        _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
        _calcCOG - If true calculate the center of gravity (mass) to initialize registration.
        _bracketBound - Sets minimum and maximum limits as initial guess -+ unit_tolerance[i]*bracketBound.
        _baseNumIter - Limits the number of iterations of Powell's algorithm. maxIter in the call to Powell's will be an integer multiple of baseNumIter
        _numMinima - Number of minima from level 8 to test at level 4

    • 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

      • 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

      • testBounds3D

        protected boolean testBounds3D​(double[] point,
                               java.lang.String message)
        DOCUMENT ME!
        Parameters:
        point - DOCUMENT ME!
        message - DOCUMENT ME!
        Returns:
        DOCUMENT ME!

      • subsampleBy2

        private 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

        private 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.

      • checkMinimum

        private boolean checkMinimum​(AlgorithmConstrainedELSUNCOAR3D.MatrixListItem item)
        Check if the given minimum is within the translation and rotation bounds.
        Parameters:
        item - MatrixListItem
        Returns:
        good true if minimum is in bounds.

      • 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 AlgorithmConstELSUNC 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

        private java.util.Vector<AlgorithmConstrainedELSUNCOAR3D.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. Removes those items that are outisde 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

        private java.util.Vector<AlgorithmConstrainedELSUNCOAR3D.MatrixListItem> levelFour​(ModelSimpleImage ref,
                                                                                   ModelSimpleImage input,
                                                                                   java.util.Vector<AlgorithmConstrainedELSUNCOAR3D.MatrixListItem> minima,
                                                                                   java.util.Vector<AlgorithmConstrainedELSUNCOAR3D.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 AlgorithmConstrainedELSUNCOAR3D.MatrixListItem levelOne​(ModelSimpleImage ref,
                                                                ModelSimpleImage input,
                                                                AlgorithmConstrainedELSUNCOAR3D.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 AlgorithmConstrainedELSUNCOAR3D.MatrixListItem levelTwo​(ModelSimpleImage ref,
                                                                ModelSimpleImage input,
                                                                java.util.Vector<AlgorithmConstrainedELSUNCOAR3D.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.