[Insight-developers] itkAzimuthElevationToCartesianTransform

[Miller, James V (CRD)]

This derivation was a stop-gap so that they could put an ultrasound
transform into an Image (which only allows an Affine).  The transform
is not an affine but since we did not have the appropriate superclass
that can be passed into an image, this was the temporary solution.




-----Original Message-----
From: Luis Ibanez [mailto:luis.ibanez@kitware.com]
Sent: Wednesday, February 20, 2002 7:39 PM
To: insight-developers@public.kitware.com
Subject: [Insight-developers] itkAzimuthElevationToCartesianTransform



Hi,

Some template parameters are being removed from the Transforms.

Among the transforms there is

               itkAzimuthElevationToCartesianTransform

deriving from 

               itkAffineTransform.

Is there any advantage in deriving from itkAffineTransform
instead of deriving from the generic itkTransform ?

What are the parameters of this transformation ?
maybe the orientation of the poles ?
and the origin of the azimuth and altitud angles ?
We may need to define SetParameters() and a GetJacobian()
methods for it.

There are also methods for sampling the input space of the
transform and getting the images of the sampling points
at the output space. This can be quite useful but maybe this
functionality should be implemented in a filter rather than
a Transform.

This transform also have methods for making it work
Forwards or Backwards, In principle the API of the
transforms uses : Transform() and BackTransform()
for this purpouse.


This class will be fundamental for registering Ultrasound
images with rectangular grid images like CT or MRI.

We may want to have a 2D and a 3D version (the current
one is 3D).

In fact the 2D could be implemented as a ND cylindrical
transform  that maps 2 of the dimension into a circle and
left the others untouched.  In that way the same class will
serve to map 2D ultrasond to 2D rectangular-grid images,
and 2D ultrasound parallel sequences to 3D rectangular-gird
images.

The current 3D version could also be generalized to ND by
mapping 3 of the N dimensions into a sphere leaving the
other dimensions untouched. That will serve for mapping
time sequences of 3D ultrasound to time sequences of
3D rectangular-grid images (because time will still be
rectilinear).


Would these changes disrupt the current use of this class ?



   Thanks


              Luis





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