ITK/Release 4/Enhancing Image Registration Framework/Tcon 2010-09-07: Difference between revisions
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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 | ||
*** | *** 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 | ||
** | ** 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). | ||
Line 29: | Line 29: | ||
** Hide it in the iterator ? | ** Hide it in the iterator ? | ||
** Implement a Random iterator for Meshes (random point access) ? | ** Implement a Random iterator for Meshes (random point access) ? | ||
** Unify the representation of Meshes and Images ? | ** 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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** Use an intermediate space to which both images are registered | ** 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. | ** 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.
- 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.