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14 February 2012 Consistent estimation of shape parameters in statistical shape model by symmetric EM algorithm
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In order to fit an unseen surface using statistical shape model (SSM), a correspondence between the unseen surface and the model needs to be established, before the shape parameters can be estimated based on this correspondence. The correspondence and parameter estimation problem can be modeled probabilistically by a Gaussian mixture model (GMM), and solved by expectation-maximization iterative closest points (EM-ICP) algorithm. In this paper, we propose to exploit the linearity of the principal component analysis (PCA) based SSM, and estimate the parameters for the unseen shape surface under the EM-ICP framework. The symmetric data terms are devised to enforce the mutual consistency between the model reconstruction and the shape surface. The a priori shape information encoded in the SSM is also included as regularization. The estimation method is applied to the shape modeling of the hippocampus using a hippocampal SSM.
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Kaikai Shen, Pierrick Bourgeat, Jurgen Fripp, Fabrice Meriaudeau, and Olivier Salvado "Consistent estimation of shape parameters in statistical shape model by symmetric EM algorithm", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140R (14 February 2012); doi: 10.1117/12.911746;

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