Sparse Statistical Shape Modelling
The provided code is based on the work by A. Gooya et al.  which proposes a Gaussian mixture model based approach to training statistical shape models (SSMs). The novel feature of the proposed approach is the application of a symmetric Dirichlet prior on the mixture coefficients to enforce sparsity and search over a continuous space for the optimal number of Gaussian components, to address the common issue of over or under-fitting. Additionally, we provide code to reconstruct surfaces from the unstructured point sets generated, following SSM training.
 Gooya, Ali, Christos Davatzikos, and Alejandro F. Frangi. "A bayesian approach to sparse model selection in statistical shape models." SIAM Journal on Imaging Sciences 8.2 (2015): 858-887.
Size of download
People who contribute to this work
Nishant Ravikumar, Department of Mechanical Engineering, The University of Sheffield
Ali Gooya Electrical and Electronic Engineering Department, The University of Sheffield
Alejandro F. Frangi Electrical and Electronic Engineering Department, The University of Sheffield