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

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* Cory Quammen
* Cory Quammen
* Gabe Hart
* Gabe Hart
* Nick Tustison
* Andy  
* Andy  
* Brian Avants
* Brian Avants
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** How to compose multiple transforms into a single
** How to compose multiple transforms into a single
*** ResampleImageFilter only deals with itk::Transform
*** ResampleImageFilter only deals with itk::Transform
*** WrapImageFilter only deals with a deformation field
*** 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.
*** 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)
* Potential Names (for this new class)
** WarpImageMultiTransformFilter
** WarpImageMultiTransformFilter
** ConcatenatedTransformImageTransformFilter
** ConcatenatedMappingImageTransformFilter
* See the Gaussian down-sampling as another Transformation
* See the Gaussian down-sampling as another Transformation
** Avoid storing the entire pyramid in memory (saving memory consumption).
** 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? )
* Projective transforms for CV community
** http://ibia.umit.at/ResearchGroup/Phil/web/Simple2D3DRegistrationFramework.html
*  Maximize  MI(  I(x) , J(T(x)) )  by gradient methods:
**  \partial Metric /  \partial Image  \partial Image / \partial Transform \partial Transform / \partial x


== Use Cases ==
== Use Cases ==
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** Deformation Field
** Deformation Field
** Without having to do more than one interpolation (e.g. via concatenation of Transform).
** 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 ?

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 ?