TY - GEN
T1 - A Bayesian approach for construction of sparse statistical shape models using Dirichlet distribution
AU - Gooya, Ali
AU - Mousavi, Elaheh
AU - Davatzikos, Christos
AU - Liao, Hongen
PY - 2013
Y1 - 2013
N2 - Statistical shape models (SSMs) made using point sets are important tools to capture the variations within shape populations. One popular method for construction of SSMs is based on the Expectation-Maximization (EM) algorithm which establishes probabilistic matches between the model and training points. In this paper, we propose a novel Bayesian framework to automatically determine the optimal number of the model points. We use a Dirichlet distribution as a prior to enforce sparsity on the mixture weights of Gaussians. Insignificant model points are determined and pruned out using a quadratic programming technique. We apply our method to learn a sparse SSM from 15 manually segmented caudate nuclei data sets. The generalization ability of the proposed model compares favorably to a traditional EM based model.
AB - Statistical shape models (SSMs) made using point sets are important tools to capture the variations within shape populations. One popular method for construction of SSMs is based on the Expectation-Maximization (EM) algorithm which establishes probabilistic matches between the model and training points. In this paper, we propose a novel Bayesian framework to automatically determine the optimal number of the model points. We use a Dirichlet distribution as a prior to enforce sparsity on the mixture weights of Gaussians. Insignificant model points are determined and pruned out using a quadratic programming technique. We apply our method to learn a sparse SSM from 15 manually segmented caudate nuclei data sets. The generalization ability of the proposed model compares favorably to a traditional EM based model.
UR - http://www.scopus.com/inward/record.url?scp=84890929113&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890929113&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40843-4_16
DO - 10.1007/978-3-642-40843-4_16
M3 - Conference contribution
AN - SCOPUS:84890929113
SN - 9783642408427
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 144
EP - 152
BT - Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions - 6th Int. Workshop, MIAR 2013 and 8th Int. Workshop, AE-CAI 2013, Held in Conjunction with MICCAI 2013, Proc.
T2 - 6th International Workshop on Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, MIAR 2013 and 8th International Workshop, AE-CAI 2013, Held in Conjunction with MICCAI 2013
Y2 - 22 September 2013 through 22 September 2013
ER -