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: B-Spline Automatic Registration
Transformation options
The following is a discussion of the dialog options which control each registration
pass, one set of options per pass:
B-spline Degree (same for all axes): This is the degree of the B-spline
to use for all axes of the image. Even though the BSplineRegistration2Df
and BSplineRegistration3Df classes allow for specification of separate
degrees for each axis, this dialog restricts the degrees to be the same. The
drop list of options show the values of 1 (linear), 2 (quadratic), 3 (cubic),
and 4 which are available. Although, the underlying BSpineBasisf class
supports any degree, this dialog restricts the degrees to these values.Choosing
a smaller degree executes faster than a larger degree, but a higher
degree offers a higher order of continuity.
B-spline Control Points (same for all axes): This is the number of B-
spline control points to use for all axes of the image. Even though the
BSplineRegistration2Df and BSplineRegistration3Df classes allow
for specification of separate number of control points for each axis, this dialog
restricts the numbers of control points to be the same. Initially, the control
points are evenly spaced in each dimension, and the registration
algorithm moves the interior control points to minimize the error difference
between the target image and the registered source image. The minimum
number of control points is the larger of the number 2 and the degree of the
B-spline. This dialog option limits the maximum number of control points
to one-half of the number of samples in the smallest dimension. Choosing
more control points generally results in better overall fitting, but a limitation
may be that the control points will be closer to each other and that will
limit how much a control point can move to minimize the error (in order to
satisfy the constraint that an interior control point must be within the
bounding polygon/polyhedron formed by its neighboring control points).
Also, since choosing more control points means that control points will be
closer to each other, it may be helpful to reduce the maximum number of
steps for searching for the gradient descent minimum.
Choosing more control points rather than fewer should result in a longer
execution time. Even though more control points means fewer samples (for
the same size input image) are affected by a single control point, the fact
that there are more control points to move and each one requires a gradient
descent sample search for a minimum error most likely results in the longer
execution time.
MIPAV User’s Guide, Volume 2, Algorithms 124
12/5/08