| Summary: | The automated analysis of medical images plays an increasingly significant part in many clinical applications. Image registration is an important and widely used technique in this context. Examples of its use include, but are not limited to: longitudinal studies, atlas construction, statistical analysis of populations and automatic or semi-automatic parcellation of structures. Although image registration has been subject of active research since the 1990s, it is a challenging topic with many issues that remain to be solved. This thesis seeks to address some of the open challenges of image registration by proposing fast and robust methods based on the widely utilised and well established registration framework of B-spline Free-Form Deformations (FFD). In this work, a statistical method has been incorporated into the FFD model, in order to obtain a fast learning-based method that produces results that are in accordance with the underlying variability of the population under study. Several comparisons between different statistical analysis methods that can be used in this context are performed. Secondly, a method to improve the convergence of the B-Spline FFD method by learning a gradient projection using principal component analysis and linear regression is proposed. Furthermore, a robust similarity measure is proposed that enables the registration of images affected by intensity inhomogeneities and images with pathologies, e.g. lesions and/or tumours. All the methods presented in this thesis have been extensively evaluated using both synthetic data and large datasets of real clinical data, such as Magnetic Resonance (MR) images of the brain and heart.
|
|---|
