Establishing the average shape and spatial
variability for a set of similar
anatomical brains is important for detecting and
discriminating morphological differences
between populations. This may be done using deformable
templates to synthesize a 3D
MRI image of the average brain from a set of MRI images
collected from a population of
similar brains. This talk will present a method for
empirically estimating the average shape
and variation of a set of 3D medical image data sets
collected from a homogeneous
population of topologically similar anatomies. Next, the
error associated with the choice of
template selected from the population used to synthesize
the average population shape
will be examined. Population averages have been
synthesized for a population of six
normal adult brains using a consistent linear-elastic
image registration algorithm. Each
data set from the populations was used as the template
to
synthesize a population
average. This resulted in six different population
averages for the brain population. The
displacement variance distance from a brain within the
population to the other brains in the
population ranged from 9.3 to 14.2 mm^2 while the
displacement variance distance from
the synthesized average brains to the population ranged
from 3.2 to 3.6 mm^2. These
results suggest that there was no significant difference
between the choice of template
with respect to the shape of the synthesized average
data
set.
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