ABSTRACT OF THESIS submitted by Roy Samuel Schestowitz
for the Degree of Doctor of Philosophy and entitled ``Unifying Models and Registration: A Framework for Model-based Registration and Non-rigid Registration Assessment''
Statistical models of shape and appearance are widely used for analysis
of biomedical images. Two deficiencies of these models are that they
require consistent annotation of a large number of images in order
to be built, and having built such models, it is then difficult to
reason about their validity, let alone assess their quality. Herein,
a method is described which addresses both problems and establishes
a unified solution. In order to construct models reliably and rapidly,
corresponding structures must be brought into a state where dense
overlap across images is obtained. Image registration is the mechanism
whereby a set of images can be analysed in a common frame of reference
and models then derived from it. The thesis provides a solution to
the problem where there is a recurring need to compare such models.
It extends the method so as to provide an image registration assessment
method which does not require ground-truth data. The thesis also deals
with a complementary case where images are registered by minimising
the complexity of models. Overall, the proposed framework can be perceived
as one which combines registration and modelling, taking advantage
of the fact that the ideas behind them are inherently the same. Registration
provides correspondence across images; given that correspondence,
models of appearance can be built and registration then assessed,
without the need for ground-truth data.