Multi-object analysis of volume, pose, and shape using statistical discrimination

Traditionally, shape analysis is used for representation and statistical analysis of single objects, and often the goal is to discriminate between two populations of objects. Medial representations are geometric models that describe anatomical structures by defining the topology and shape of a structure's skeleton and then deriving the geometry of the structure's boundary from the skeleton. In this talk, we will discuss how "m-reps" were used in a certain clinical pediatric autism study involving sub-cortical brain structures. In order to differentiate between children with autism and typically developing children, we present a method called "distance weighted discrimination" (DWD) for analyzing multiple objects. It is a method similar to support vector machines and is a process for finding the best hyperplane that separates two populations. We will discuss different features, like shape, volume, and pose, to find out which is more significant for discriminating populations.