Volume 2, Algorithms - mipav - National Institutes of Health

M I P A V
M e d i c a l I m a g e P r o c e s s i n g, A n a l y s i s, & V i s u a l i z a t i o n
Chapter 2, Using MIPAV Algorithms: Optimized Automatic Registration 3D
Post optimization
8 Using the same interpolation methods as described in Step 2 and the
optimal transformation determined above, the method transforms the
reference image into the same coordinate system of the target image.
Following Sections 1.2-1.5 describe in detail the algorithms and methods
used to perform Steps 1-8 of the optimization routine.
PRE OPTIMIZATION
Blurring
The registration algorithm first, determines the minimum resolution for
each dimension of the reference and target images. The reference image is
blurred to the resolution of the target image if one of the resolutions in a
dimension of the target image is 50% or larger than the corresponding resolution
in the reference image. Likewise, the target image is blurred to the
resolution of the reference image if one of the resolutions in a dimension of
the reference image is 50% or larger than the corresponding resolution in
the target image. It is possible (but not likely) that both the reference and
target images will be blurred.
See also “Resampling Interpolation” on page 605.
Resampling
Once this isotropic 1mm resolution images have been obtained, the
subsampled versions are also created. The sub-sampling algorithm then
simply keeps every n-th point (that is, 2, 4 or 8) on the lattice in each
direction. Therefore, the new volume contains 1/n3 as many points as the
original.
Resampling can be performed for x, y, and z axes for 3D images, or for only x
and y for 2D images.
MIPAV User’s Guide, Volume 2, Algorithms 599
12/5/08