title: Unified segmentation.
abstract: A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
authors.affiliation: Wellcome Department of Imaging Neuroscience, 12 Queen Square, London, WC1N 3BG, UK. john@fil.ion.ucl.ac.uk
authors.forename: John
authors.initials: J
authors.lastname: Ashburner
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Friston
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D003627:Data Interpretation, Statistical; D017143:Fuzzy Logic; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D011338:Probability Theory