Learning 2D Shape Models

A new fully automated shape learning method is presented. It is based on clustering a set of training shapes in the original shape space (de ned by the coordinates of the contour points) and performing a Procrustes analysis on each cluster to obtain cluster prototypes and information about shape variation. The main di erence from previously reported methods is that the training set is rst automatically clustered and those shapes considered to be outliers are discarded. The second di erence is in the manner in which registered sets of points are extracted from each shape contour. As a direct application of our shape learning method, an 11-structure shape model of brain substructures was extracted from MR image data, an eigen-shape model was automatically trained, and employed to segment several MR brain images not present in the shape-training set. A quantitative analysis of our shape registration approach, within the main cluster of each structure, shows that our results compare very well to those achieved by manual registration; achieving an average rms error of about 1 pixel. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual shape registration and analysis.