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      Unified segmentation.

      Neuroimage

      Algorithms, Normal Distribution, Nonlinear Dynamics, Models, Statistical, Models, Neurological, Magnetic Resonance Imaging, Likelihood Functions, statistics & numerical data, Image Processing, Computer-Assisted, Fuzzy Logic, Data Interpretation, Statistical, Brain Mapping, Probability Theory

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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.

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          Journal
          10.1016/j.neuroimage.2005.02.018
          15955494

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