# ITK/Release 4/Enhancing Image Registration Framework/Tcon 2010-09-07

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# 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.

- How to compose multiple transforms into a single
- 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? )

- Projective transforms for CV community

- 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 ?

- Selecting a subset of parameters from a larger set.