An introduction to variational methods for graphical models
Learning in graphical models
Convergence Theorems for Generalized Alternating Minimization Procedures
The Journal of Machine Learning Research
Fully Bayesian Joint Model for MR Brain Scan Tissue and Structure Segmentation
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Shape analysis and fuzzy control for 3d competitive segmentation of brain structures with level sets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We consider a general modelling strategy to handle in a unified way a number of tasks essential to MR brain scan analysis. Our approach is based on the explicit definition of a Conditional Random Field (CRF) model decomposed into components to be specified according to the targeted tasks. For a specific illustration, we define a CRF model that combines robust-to-noise and to nonuniformity Markovian tissue and structure segmentations with local affine atlas registration. The evaluation performed on both phantoms and real 3T images shows good results and, in particular, points out the gain in introducing registration as a model component. Besides, our modeling and estimation scheme provide general guidelines to deal with complex joint processes for medical image analysis.