Bayesian template estimation in computational anatomy.
Templates play a fundamental role in Computational Anatomy. In this paper, we present a Bayesian model for template estimation. It is assumed that observed images I(1), I(2),...,I(N) are generated by shooting the template J through Gaussian distributed random initial momenta theta(1), theta(2),...,theta(N). The template is J modeled as a deformation from a given hypertemplate J(0) with initial momentum mu, which has a Gaussian prior. We apply a mode approximation of the EM (MAEM) procedure, where the conditional expectation is replaced by a Dirac measure at the mode. This leads us to an image matching problem with a Jacobian weight term, and we solve it by deriving the weighted Euler-Lagrange equation. The results of template estimation for hippocampus and cardiac data are presented.
Ma
Jun
J
Center for Imaging Science and Department of Biomedical Engineering, The Johns Hopkins University, 320 Clark Hall, Baltimore, MD 21218, USA. junma@cis.jhu.edu
Miller
Michael I
MI
Trouvé
Alain
A
Younes
Laurent
L
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D006624:Hippocampus / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity