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

Class AlgorithmRegOAR35D

java.lang.Object
java.lang.Thread
gov.nih.mipav.model.algorithms.AlgorithmBase
gov.nih.mipav.model.algorithms.registration.AlgorithmRegOAR35D
All Implemented Interfaces:
ActionListener, WindowListener, Runnable, EventListener
public class AlgorithmRegOAR35D 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)

  • Field Details

    • ADJACENT

      private static final int ADJACENT
      DOCUMENT ME!
      See Also:

    • AVERAGE

      private static final int AVERAGE
      DOCUMENT ME!
      See Also:

    • minimumZForSub

      private static final int minimumZForSub
      DOCUMENT ME!
      See Also:

    • 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 AlgorithmRegOAR35D.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.

    • 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 Powell optimization. maxIter in the call to Powell's will be an integer multiple of baseNumIter

    • baseNumIter

      private int baseNumIter
      Limits number of iterations in Powell optimization. maxIter in the call to Powell's 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!

    • doJTEM

      private boolean doJTEM
      Turns the full version of Powell's algorithm on/off. When off the JTEM line minimization used.

    • doMultiThread

      private boolean doMultiThread

    • TransMatsInumber

      private int[] TransMatsInumber

  • Constructor Details

    • AlgorithmRegOAR35D

      public AlgorithmRegOAR35D(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)
      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 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

    • AlgorithmRegOAR35D

      public AlgorithmRegOAR35D(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)
      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 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 Details

    • setJTEM

      public void setJTEM(boolean bOn)
      Turns the full version of Powell's algorithm on/off.
      Parameters:
      bOn -

    • 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

    • getArrayTransMatrix2

      public TransMatrix[] getArrayTransMatrix2()
      Sorts the array of transformation matricies and returns the sorted array. If the sub-volumes are sorted to a sub-volume within the 4D image, then the matrix for the reference volume is the identity matrix.
      Returns:
      Array of TransMatrix, one for each sub-volume in the 4D image.

    • getArrayTransMatrix

      public TransMatrix[] getArrayTransMatrix()
      Sorts the array of transformation matricies and returns the sorted array. If the sub-volumes are sorted to a sub-volume within the 4D image, then the matrix for the reference volume is the identity matrix.
      Returns:
      Array of TransMatrix, one for each sub-volume in the 4D image.

    • 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 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 AlgorithmPowell 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 Vector<AlgorithmRegOAR35D.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 Vector<AlgorithmRegOAR35D.MatrixListItem> levelFour(ModelSimpleImage ref, ModelSimpleImage input, Vector<AlgorithmRegOAR35D.MatrixListItem> minima, Vector<AlgorithmRegOAR35D.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 AlgorithmRegOAR35D.MatrixListItem levelOne(ModelSimpleImage ref, ModelSimpleImage input, AlgorithmRegOAR35D.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 AlgorithmRegOAR35D.MatrixListItem levelTwo(ModelSimpleImage ref, ModelSimpleImage input, Vector<AlgorithmRegOAR35D.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!