Invited Talk by Professor Kilian Pohl:
Active Mean Fields: Evolving Curves via an Explicit Probabilistic Representation
Abstract
In this talk we develop a new curve evolution formulation for estimating the posterior distribution of objects in images. Similar to level sets, we describe the segmentation of images via a conventional likelihood model combined with a curve prior on boundaries. Unlike level sets, the curves are encoded via the logarithm-of-odds representing the posterior distribution on labels in an unconstrained vector space. The posterior distributions are sought via the Mean Field approach. By choosing a different representation and optimization, our framework sidesteps many of the issues traditionally associated with level set implementations. For example, applications with more than two labels are easily accommodated, as the label assignment is accomplished by the Maximum A Posteriori rule, so there are no problems of overlap or vacuum between level set functions. We demonstrate the effectiveness of this technology on a synthetic noisy image and real 3D medical scans.
Short Bio
Kilian M Pohl is as an Assistant Professor at the Section for Biomedical Image Analysis, Department of Radiology, University of Pennsylvania. His main research area is computational image analysis with an emphasis on studying statistical models from a Bayesian perspective. For more information you can visit his web site at
https://www.rad.upenn.edu/sbia/Kilian.Pohl/
