Unified segmentation
J Ashburner, KJ Friston - neuroimage, 2005 - Elsevier
neuroimage, 2005•Elsevier
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.
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.
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.
Elsevier
