Abstract

We describe a method for automatically building statistical shape models from a training set of exam- ple boundaries / surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between all members of a set of training shapes. Often this is achieved by locating a set of qandmarks manually on each training image, which is time-consuming and subjective in 2D, and almost impossible in 3D. We describe how shape models can be built automatically by posing the correspondence problem as one of finding the parameterization for each shape in the training set. We select the set of parameterizations that build the best model. We define best as that which min- imizes the description length of the training set, arguing that this leads to models with good compactness, specificity and generalization ability. We show how a set of shape parameterizations can be represented and manipulated in order to build a minimum description length model. Results are given for several different training sets of 2D boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking - the current gold standard. We also show that the method can be extended straightforwardly to 3D.

Citations