Title: Object Representation: Geometry, Statistics, and Data Structures

Speaker: Stephen M. Pizer, Kenan Professor of Computer Science, Radiology, Radiation Oncology, & Biomedical Engineering, University of North Carolina, Chapel Hill.

Date: Sunday, November 4, 2001

Time: Two consecutive sessions ... 10-12 noon and 2:00-4:00 pm

Place: 2229 Seamans Center

Abstract:

The geometry of anatomic objects can be described using a variety of different atoms: voxels, landmarks, boundary locations with or without orientation, and medial descriptors. This geometry can be used as the basis for model-based object segmentation from a (typically 3D) image, for image registration, or for characterization of an object's shape. Probability distributions for geometry or shape can be used as priors in posterior optimization methods for segmentation or registration, or they can be used to discriminate classes of shapes, such as normal and pathological variants of an object. In this tutorial I will describe the various atoms and the sampled and parametrized object descriptions that can be built from them, as well as the spatial correspondences across deformation to which they lead, and I will compare these descriptions. Issues of the scales of representation provided and the need for multiple scales will be faced. The means of representing and training probability distributions of geometry will be faced. Metrics for geometric typicality based on pure geometry and metrics based on geometry and probability will be described.