Rueckert et al.  describe statistical deformation models (SDM's) which are in essence surprisingly similar to active appearance models. Much work has concentrated on using the knowledge and techniques from each one of these two to establish a more powerful framework of full appearance statistical models. The work is described in Section 5 with reference to research that is associated with the GC. An exclusive introduction to Rueckert's work will shed some light over the current registration concepts which future research relies upon.
Non-rigid registration methods have been applied in several medical domains of expertise. Among these is the renowned brain analysis task, contrast-enhanced MR mammography and segmentation and tracking of the heart. The procedures currently employed are inclined to follow higher-order entropy measures that will not be delved any further. Rueckert's homepage which is listed in Appendix A gives the full details and references. The next section on information theory explains in brevity some of the basic ideas behind these so-called entropy measures.
The success of temporal non-rigid image registration method is dependent upon two factors:
Change in organs due to resection (craniotomy being a frequently-encountered scenario), expansion, movement etc. is often modelled using thin-plate splines and the motion of organs can be handled using free-form deformation (FFD) which are based on B-splines. Prior to this embedment of high-order functions, the effects of rigid-body motion is annulled by Euclidean transformation. Similarity measures guide this process of rigid registration just as well. It is the technical description of the algorithms used that proves why these methods, which are used in Guy's Hospital, are extremely effective. Future reports will address the finer technical details that express the operation of the algorithms.