Prior knowledge regularization in statistical medical image tasks

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

The estimation of the covariance matrix is a pivotal step inseveral statistical tasks. In particular, the estimation becomes challeng-ing for high dimensional representations of data when few samples areavailable. Using the standard Maximum Likelihood estimation (MLE)when the number of samples are lower than the dimension of the datacan lead to incorrect estimation e.g. of the covariance matrix and subse-quent unreliable results of statistical tasks. This limitation is normallysolved by the well-known Tikhonov regularization adding partially anidentity matrix; here we discuss a Bayesian approach for regularizing thecovariance matrix using prior knowledge. Our method is evaluated forreconstructing and modeling vertebra and cartilage shapes from a lowerdimensional representation and a conditional model. For these centralproblems, the proposed methodology outperforms the traditional MLEmethod and the Tikhonov regularization.
TitelProceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis
Antal sider12
StatusUdgivet - 2009
BegivenhedInternational Conference on Medical Image Computing and Computer Assisted Intervention - London, Storbritannien
Varighed: 20 sep. 200924 sep. 2009
Konferencens nummer: 12


KonferenceInternational Conference on Medical Image Computing and Computer Assisted Intervention

ID: 21235760