Active Mean Fields: Solving the Mean Field Approximation in the Level Set Framework

Abstract. We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries, and an approximate posterior distribution on labels is sought via the Mean Field approach. Optimizing the resulting estimator by gradient descent leads to a level set style algorithm where the level set functions are the logarithm-of-odds encoding of the posterior label probabilities in an unconstrained linear vector space. Applications with more than two labels are easily accommodated. The label assignment is accomplished by the Maximum A Posteriori rule, so there are no problems of "overlap" or "vacuum". We test the method on synthetic images with additive noise. In addition, we segment a magnetic resonance scan into the major brain compartments and subcortical structures.

• Type: Conference
• Year: 2007
• Where: IPMI
• Authors: Kilian M. Pohl, Ron Kikinis, William M. Wells III
• Keywords: Approximate Posterior Distribution, IPMI 2007, Medical Imaging, Posterior Label Probabilities, Posterior Probability