BMVC 2004, Kingston, 7th-9th Sept, 2004

Diffeomorphic Statistical Shape Models
T.F. Cootes, C.J. Twining and C.J. Taylor (University of Manchester)

We describe a method of constructing parametric statistical models of
shape variation which can generate continuous diffeomorphic (non-folding)
deformation �elds. Traditional statistical shape models are constructed by
analysis of the positions of a set of landmark points. Here we analyse the parameters
of continuous warp �elds, constructed by composing simple parametric
diffeomorphic warps. The warps are composed in such a way that
the deformations are always de�ned in a reference frame. This allows the
parameters controlling the deformations to be meaningfully compared from
one example to another. A linear model is learnt to represent the variations
in the warp parameters across the training set. This model can then be used
to generalise the deformations. Models can be built either from sets of annotated
points, or from unlabelled images. In the latter case, we use techniques
from non-rigid registration to construct the warp fields deforming a reference
image into each example. We describe the technique in detail and give
examples of the resulting models.
(pdf article)