Active shape models—their training and application
Computer Vision and Image Understanding
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A unified information-theoretic approach to groupwise non-rigid registration and model building
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
A bayesian cost function applied to model-based registration of sub-cortical brain structures
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Sample Sufficiency and Number of Modes to Retain in Statistical Shape Modelling
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
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A variety of different methods of finding correspondences across sets of images to build statistical shape models have been proposed, each of which is likely to result in a different model. When dealing with large datasets (particularly in 3D), it is difficult to evaluate the quality of the resulting models. However, if the different methods are successfully modelling the true underlying shape variation, the resulting models should be similar. If two different techniques lead to similar models, it suggests that they are indeed approximating the true shape change. In this paper we explore a method of comparing statistical shape models by evaluating the Bhattacharya overlap between their implied shape distributions. We apply the technique to investigate the similarity of three models of the same 3D dataset constructed using different methods.