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Unified segmentation
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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.