  We present a framework for shape matching in computational anatomy allowing
users control of the degree to which the matching is diffeomorphic. This
control is given as a function defined over the image and parameterises the
template deformation. By modelling localised template deformation we have a
mathematical description of growth only in specified parts of an image. The
location can either be specified from prior knowledge of the growth location or
learned from data. For simplicity, we consider landmark matching and infer the
distribution of a finite dimensional parameterisation of the control via Markov
chain Monte Carlo. Preliminary numerical results are shown and future paths of
investigation are laid out. Well-posedness of this new problem is studied
together with an analysis of the associated geodesic equations.
