[elastix] Problems using initial transform and masks

Andriy Fedorov fedorov at bwh.harvard.edu
Mon Jun 13 15:59:36 CEST 2011


Hi,

I am trying to register two images using initial transformation and
floating+fixed masks. I created the initial transformation manually
outside elastix, but I did verify that the moving image resampled with
transformix using my transformation overlaps with the fixed image as
expected. My masks are both defined by voxels set to 1, in the same
space as the fixed/floating images. The masks have good overlap when
the initial transform is applied to the moving mask.

My parameter file is based on the one used in the example. I changed
some of the parameters to handle direction cosines, and turn off
automatic initialization. The parameter file is attached. The command
line is this:

elastix -f fixed.nrrd -m moving.nrrd -out Results -p
Parameters/parameters_Affine-initialized.txt -fMask fixed-mask.nrrd
-mMask moving-mask.nrrd -t0 initial_tfm.txt

The problem is that when I run elastix, I always get this error

itk::ExceptionObject (0x37d1190)
Location: "ElastixTemplate - Run()"
File: /home/sklein/tk/elastix/exports/tags/elastix_04_3/src/Components/Optimizers/AdaptiveStochasticGradientDescent/elxAdaptiveStochasticGradientDescent.hxx
Line: 1287
Description: itk::ERROR: AdaptiveStochasticGradientDescent(0x30f29a0):
No valid voxels (0/1000) found to estimate the
AdaptiveStochasticGradientDescent parameters.

Error occurred during actual registration.

If I don't use masks, the problem goes away.

Both the fixed and moving images were acquired obliquely (direction
cosines are not aligned with the voxel grid). I am wondering if this
case is handled correctly when the masks are used in registration.

I would appreciate any help in debugging this problem.

--
Andriy Fedorov, Ph.D.

Research Fellow
Brigham and Women's Hospital
Harvard Medical School
75 Francis Street
Boston, MA 02115 USA
fedorov at bwh.harvard.edu
(617) 525-6258 (office)
-------------- next part --------------
// Example parameter file for affine registration
// C-style comments: //

// The internal pixel type, used for internal computations
// Leave to float in general. 
// NB: this is not the type of the input images! The pixel 
// type of the input images is automatically read from the 
// images themselves.
// This setting can be changed to "short" to save some memory
// in case of very large 3D images.
(FixedInternalImagePixelType "float")
(MovingInternalImagePixelType "float")

// The dimensions of the fixed and moving image
// NB: This has to be specified by the user. The dimension of
// the images is currently NOT read from the images.
// Also note that some other settings may have to specified
// for each dimension separately.
(FixedImageDimension 3)
(MovingImageDimension 3)

// Specify whether you want to take into account the so-called
// direction cosines of the images. Recommended: true.
// In some cases, the direction cosines of the image are corrupt,
// due to image format conversions for example. In that case, you 
// may want to set this option to "false".
(UseDirectionCosines "true")

// **************** Main Components **************************

// The following components should usually be left as they are:
(Registration "MultiResolutionRegistration")
(Interpolator "BSplineInterpolator")
(ResampleInterpolator "FinalBSplineInterpolator")
(Resampler "DefaultResampler")

// These may be changed to Fixed/MovingSmoothingImagePyramid.
// See the manual.
(FixedImagePyramid "FixedRecursiveImagePyramid")
(MovingImagePyramid "MovingRecursiveImagePyramid")

// The following components are most important:
// The optimizer AdaptiveStochasticGradientDescent (ASGD) works
// quite ok in general. The Transform and Metric are important
// and need to be chosen careful for each application. See manual.
(Optimizer "AdaptiveStochasticGradientDescent")
(Transform "AffineTransform")
(Metric "AdvancedMattesMutualInformation")

// ***************** Transformation **************************

// Scales the affine matrix elements compared to the translations, to make
// sure they are in the same range. In general, it's best to  
// use automatic scales estimation:
(AutomaticScalesEstimation "true")

// Automatically guess an initial translation by aligning the
// geometric centers of the fixed and moving.
(AutomaticTransformInitialization "false")

// Whether transforms are combined by composition or by addition.
// In generally, Compose is the best option in most cases.
// It does not influence the results very much.
(HowToCombineTransforms "Compose")

// ******************* Similarity measure *********************

// Number of grey level bins in each resolution level,
// for the mutual information. 16 or 32 usually works fine.
// You could also employ a hierarchical strategy:
//(NumberOfHistogramBins 16 32 64)
(NumberOfHistogramBins 32)

// If you use a mask, this option is important. 
// If the mask serves as region of interest, set it to false.
// If the mask indicates which pixels are valid, then set it to true.
// If you do not use a mask, the option doesn't matter.
(ErodeMask "false")

// ******************** Multiresolution **********************

// The number of resolutions. 1 Is only enough if the expected
// deformations are small. 3 or 4 mostly works fine. For large
// images and large deformations, 5 or 6 may even be useful.
(NumberOfResolutions 4)

// The downsampling/blurring factors for the image pyramids.
// By default, the images are downsampled by a factor of 2
// compared to the next resolution.
// So, in 2D, with 4 resolutions, the following schedule is used:
//(ImagePyramidSchedule 8 8  4 4  2 2  1 1 )
// And in 3D:
//(ImagePyramidSchedule 8 8 8  4 4 4  2 2 2  1 1 1 )
// You can specify any schedule, for example:
//(ImagePyramidSchedule 4 4  4 3  2 1  1 1 )
// Make sure that the number of elements equals the number
// of resolutions times the image dimension.

// ******************* Optimizer ****************************

// Maximum number of iterations in each resolution level:
// 200-500 works usually fine for affine registration.
// For more robustness, you may increase this to 1000-2000.
(MaximumNumberOfIterations 250)

// The step size of the optimizer, in mm. By default the voxel size is used.
// which usually works well. In case of unusual high-resolution images
// (eg histology) it is necessary to increase this value a bit, to the size
// of the "smallest visible structure" in the image:
//(MaximumStepLength 1.0)

// **************** Image sampling **********************

// Number of spatial samples used to compute the mutual
// information (and its derivative) in each iteration.
// With an AdaptiveStochasticGradientDescent optimizer,
// in combination with the two options below, around 2000
// samples may already suffice.
(NumberOfSpatialSamples 2048)

// Refresh these spatial samples in every iteration, and select
// them randomly. See the manual for information on other sampling
// strategies.
(NewSamplesEveryIteration "true")
(ImageSampler "Random")

// ************* Interpolation and Resampling ****************

// Order of B-Spline interpolation used during registration/optimisation.
// It may improve accuracy if you set this to 3. Never use 0.
// An order of 1 gives linear interpolation. This is in most 
// applications a good choice.
(BSplineInterpolationOrder 1)

// Order of B-Spline interpolation used for applying the final
// deformation.
// 3 gives good accuracy; recommended in most cases.
// 1 gives worse accuracy (linear interpolation)
// 0 gives worst accuracy, but is appropriate for binary images
// (masks, segmentations); equivalent to nearest neighbor interpolation.
(FinalBSplineInterpolationOrder 3)

//Default pixel value for pixels that come from outside the picture:
(DefaultPixelValue 0)

// Choose whether to generate the deformed moving image.
// You can save some time by setting this to false, if you are
// only interested in the final (nonrigidly) deformed moving image
// for example.
(WriteResultImage "true")

// The pixel type and format of the resulting deformed moving image
(ResultImagePixelType "short")
(ResultImageFormat "nrrd")



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