Segmentation of magnetic resonance images using mean field annealing
Image and Vision Computing - Special issue: information processing in medical imaging 1991
Statistical physics, mixtures of distributions, and the EM algorithm
Neural Computation
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
An introduction to variational methods for graphical models
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Improving the mean field approximation via the use of mixture distributions
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Approximating posterior distributions in belief networks using mixtures
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
WBIA '98 Proceedings of the IEEE Workshop on Biomedical Image Analysis
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
IEEE Transactions on Image Processing
The convergence of mean field procedures for MRFs [image processing]
IEEE Transactions on Image Processing
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This paper considers the use of the EM-algorithm, combined with mean field theory, for parameter estimation in Markov random field models from unlabelled data. Special attention is given to the theoretical justification for this procedure, based on recent results from the machine learning literature. With these results established, an example is given of the application of this technique for analysis of single trial functional magnetic resonance (fMR) imaging data of the human brain. The resulting model segments fMR images into regions with different 'brain response' characteristics.