### 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)