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IPMI
2007
Springer

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

12 years 2 months ago
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.
Kilian M. Pohl, Ron Kikinis, William M. Wells III
Added 16 Nov 2009
Updated 16 Nov 2009
Type Conference
Year 2007
Where IPMI
Authors Kilian M. Pohl, Ron Kikinis, William M. Wells III
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