GROUP-WISE CORRESPONDENCE OF SURFACES USING NON-PARAMETRIC REGULARISATION AND SHAPE IMAGES

Previous automatic methods for constructing statistical shape models have located the correspondence across a training set of shapes by minimising an objective function based on the minimum description length principle (MDL). Although this method has been shown to produce quantitatively better results than other methods, the computational cost is high, leading to long convergence times. In this paper, we show how topologically non-trivial training shapes can be mapped to regular grids (called shape images). This leads to a significant reduction in computational complexity, and effectively re-casts shape model-building in a similar framework to that of groupwise image registration. A further reduction in computational cost is achieved by using a non-parametric fluid-based regulariser to manipulate the correspondence between shape images. Quantitative evaluation of using shape images and fluid-based regularisation demonstrates reduced convergence time (without loss of model quality) when compared to previous methods.