Unified segmentation
Copyright policy )Unified segmentation
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.
Brain Mapping, Likelihood Functions, Models, Statistical, Models, Neurological, Normal Distribution, Probability Theory, Magnetic Resonance Imaging, Fuzzy Logic, Nonlinear Dynamics, Data Interpretation, Statistical, Image Processing, Computer-Assisted, Algorithms
Brain Mapping, Likelihood Functions, Models, Statistical, Models, Neurological, Normal Distribution, Probability Theory, Magnetic Resonance Imaging, Fuzzy Logic, Nonlinear Dynamics, Data Interpretation, Statistical, Image Processing, Computer-Assisted, Algorithms
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