The provided code is based on the work by A. Gooya et al. [1] 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.

References

[1] 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.

TAR.GZ File

http://dx.doi.org/10.6084/m9.figshare.1562325

Linux

3.33 MB