Automatic construction of 2D shape models

A procedure for automated 2D shape model design is presented. The modeling system is given a set of training example shapes defined by the coordinates of their contour points. The shapes are automatically aligned using Procrustes analysis and clustered to obtain cluster prototypes (typical objects) and statistical information about intracluster shape variation. One difference from previously reported methods is that the training set is first automatically clustered and those shapes considered to be outliers are discarded. In this way, the cluster prototypes are not distorted by outlier shapes. A second difference is in the manner in which registered sets of points are extracted from each shape contour. We propose a flexible point matching technique that takes into account both pose/scale differences as well as nonlinear shape differences between a pair of objects. The matching method is independent of the initial relative position/scale of the two objects and does not require any manually tuned parameters. Our shape model design method was used to learn 11 different shapes from contours that were manually traced in MR brain images. The resulting model was then employed to segment several MR brain images that were not included in the shape-training set. A quantitative analysis of our shape registration approach, within the main cluster of each structure, demonstrated results that compare very well to those achieved by manual registration; achieving an average registration error of about 1 pixel. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual 2D shape registration and analysis.