Assessment of Efficiency of Part & Geometry Models for Groupwise Registration

We evaluate the performance of a system which addresses the problem of building detailed models of shape and appearance of complex structures, given only a training set of representative images and some minimal manual intervention. We focus on objects with repeating structures (such as bones in the hands), which can cause normal deformable registration techniques to fall into local minima and fail. Using a sparse annotation of a single image we can construct a parts+geometry (P+G) model capable of locating a small set of features on every training image. Iterative refinement leads to a model which can locate structures accurately and reliably. The resulting sparse annotations are sufficient to initialise a dense groupwise registration algorithm, which gives a detailed correspondence between all images in the set. We demonstrate the method on a much larger set of radiographs of the hand while comparing results with that of the earlier work, we achieved a sub-millimeter accuracy in a prominent group.