Segmentation, registration, and measurement of shape variation via image object shape

A model of object shape by nets of medial and boundary primitives is justif ied as richly capturing multiple aspects of shape and yet requiring represe ntation space and image analysis work proportional to the number of primiti ves. Metrics are described that compute an object representation's prior pr obability of local geometry by reflecting variabilities in the net's node a nd link parameter values, and that compute a likelihood function measuring the degree of match of an image to that object representation. A paradigm f or image analysis of deforming such a model to optimize a posteriori probab ility is described, and this paradigm is shown to be usable as a uniform ap proach for object definition, object-based registration between images of t he same or different imaging modalities, and measurement of shape variation of an abnormal anatomical object, compared with a normal anatomical object . Examples of applications of these methods in radiotherapy, surgery, and p sychiatry are given.