In this paper we propose a new method of elastic registration of anatomical structures that bears an inner skeleton, such as the knee, hand or spine. Such a method has to deal with great degrees of variability, specially for the case of inter-subject registration; but even for the intra-subject case the degree of variability of images will be large since the structures we bear in mind are articulated. Rigid registration methods are clearly inappropriate for this problem, and well-known elastic methods do not usually incorporate the restriction of maintaining long skeletal structures straight. A new method is therefore needed to deal with such a situation; we call this new method "articulated registration". The inner bone skeleton is modeled with a wire model, where wires are drawn by connecting landmarks located in the main joints of the skeletal structure to be registered (long bones). The main feature of our registration method is that within the bone axis (specifically, where the wires are) an exact registration is guaranteed, while for the remaining image points an elastic registration is carried out based on a distance transform (with respect to the model wires); this causes the registration on long bones to be affine to all practical purposes, while the registration of soft tissue -- far from the bones -- is elastic. As a proof-of-concept of this method we describe the registration of hands on radiographs.
In this paper we describe a method for registering human hand radiographs (templates) onto a target radiograph for automatic bone age assessment. The method itself constitutes a complete methodology for bone age determination, since it performs similar tasks as the well-known Greulich-Pyle medical technique. In addition, this method is a first step towards a segmentation-by-registration procedure which pursues to carry out a detailed shape analysis of the bones of the hand with the same purpose of age determination. The method consists of two registration stages: the first one is a landmark-based procedure, with landmarks located in relevant areas of the human hand. This first stage consists of several affine transformations both applied to the whole hand and to each particular finger. The second stage is an intensity-based method which uses mutual information to correct for the differences in the finger widths between the template images and the target image.