  We describe a representation targeted for anatomic objects which is designed
to enable strong locational correspondence within object populations and thus
to provide powerful object statistics. The method generates fitted frames on
the boundary and in the interior of objects and produces alignment-free
geometric features from them. It accomplishes this by understanding an object
as the diffeomorphic deformation of an ellipsoid and using a skeletal
representation fitted throughout the deformation to produce a model of the
target object, where the object is provided initially in the form of a boundary
mesh. Via classification performance on hippocampi shape between individuals
with a disorder vs. others, we compare our method to two state-of-the-art
methods for producing object representations that are intended to capture
geometric correspondence across a population of objects and to yield geometric
features useful for statistics, and we show improved classification performance
by this new representation, which we call the evolutionary s-rep. The geometric
features that are derived from each of the representations, especially via
fitted frames, is discussed.
