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.
Center for Imaging Science and Department of Biomedical Engineering, The Johns Hopkins University, 320 Clark Hall, Baltimore, MD 21218, USA. junma@cis.jhu.edu
Jun
J
Ma
Michael I
MI
Miller
Alain
A
Trouvé
Laurent
L
Younes
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