Non-rigid registration is an important precursor to statistical analysis and machine learning in medical image analysis. It is commonly used to find correspondences between images which is a necessary first step for further processing. However, registering images which have large pose differences and/or are composed of substructures of similar appearance requires that registration be initialised carefully for the results to be valid. This work addresses both problems in the context of 3D volumetric images. We use parts-and-geometry models to automatically align images before registration proceeds. An important component of the parts are orientation-invariant descriptors computed using spin images. In the following we describe the construction of the parts-and-geometry models and how they can be incorporated into non-rigid registration. We use 3D CT images of the wrist and knee to demonstrate the effectiveness of the models at locating substructures with similar appearance, and show both qualitatively and quantitatively that initialisation with parts-and-geometry models improve the accuracy of registration. © 2013 Elsevier B.V.
- Spin images