Consistent estimation of shape parameters in statistical shape model by symmetric EM algorithm

Kaikai Shen*, Pierrick Bourgeat, Jurgen Fripp, Fabrice Meriaudeau, Olivier Salvado, Alzheimer's Disease Neuroimaging Initiative

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMedical Imaging 2012
Subtitle of host publicationImage Processing
EditorsDavid R. Haynor, Sébastien Ourselin
Place of PublicationWashington
Number of pages8
ISBN (Electronic)9780819489630
Publication statusPublished - 2012
Externally publishedYes
EventConference on Medical Imaging - Image Processing - San Diego, Canada
Duration: 6 Feb 20129 Feb 2012

Publication series

NameProceedings of SPIE
ISSN (Print)0277-786X


ConferenceConference on Medical Imaging - Image Processing
CitySan Diego

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