Difference between revisions of "ITK/Release 4/Enhancing Image Registration Framework/Tcon 2010-09-07"

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Latest revision as of 16:01, 9 December 2011

Attendees

  • Cory Quammen
  • Gabe Hart
  • Nick Tustison
  • Andy
  • Brian Avants
  • Luis Ibanez

Technical Topics

  • Transform hierarchy
    • How to compose multiple transforms into a single
      • ResampleImageFilter only deals with itk::Transform
      • WarpImageFilter only deals with a deformation field
      • A new filter is needed, that takes as input a collection of Transforms and deformation fields and apply them concatenated.
  • Potential Names (for this new class)
    • WarpImageMultiTransformFilter
    • ConcatenatedMappingImageTransformFilter
  • See the Gaussian down-sampling as another Transformation
    • Avoid storing the entire pyramid in memory (saving memory consumption).
  • Generalize the representation of an image by using a Sparse representation of the image.
    • Introduce an image sampling class that generates a Sparse image from an image.
    • Then pass this Sparse Image type to the Metrics.
    • Both for the Fixed and Moving images ?
  • How to consolidate a "smart" sampling to allow for
    • Dense sampling
    • Sparse sampling
    • Hide it in the iterator ?
    • Implement a Random iterator for Meshes (random point access) ?
    • Unify the representation of Meshes and Images ? (use SpatialObjects? )
  • Maximize MI( I(x) , J(T(x)) ) by gradient methods:
    • \partial Metric / \partial Image \partial Image / \partial Transform \partial Transform / \partial x

Use Cases

  • Be able to transform meshes (stored in VTK files) through a combination of
    • Affine Transforms
    • Deformation Field
    • Without having to do more than one interpolation (e.g. via concatenation of Transform).
  • Be able to transform Images through a combination of
    • Affine Transforms
    • Deformation Field
    • Without having to do more than one interpolation (e.g. via concatenation of Transform).
  • Perform symmetric registration (affine and deformable)(un-biased)
    • Registration in which Fixed and Moving images can be exchanged and the result of the registration will be the same.
    • Implementation: Extract the interpolation from the Metric.
    • Every metric must compute the derivative of the Metric with respect to both
      • The space of the Fixed Image
      • The space of the Moving Image
    • Use an intermediate space to which both images are registered
    • Then two transforms are computed: from the central space to each one of the two images.
  • Fit Intensity Models to images
    • E.g. Fitting a Gaussian (PSF) model to a microscopy image
    • Parametric image model
    • Some parameters from the Optimization space will correspond to the image parametric model.
  • Geometrical-Model to Image Registration
  • Better support for multiplicity (working together in a common registration problem).
    • Multiple Optimizers ?
    • Multiple Metrics ?
  • Parameter Mask
    • Selecting a subset of parameters from a larger set.
      • E.g. In a 3D affine transform enable first only the translation parameters
    • Is this related to "bounding" some (or all?) elements in the parameter array ?