Unified Segmentation

Wellcome Department of Imaging Neuroscience, 12 Queen Square, London, WC1N 3BG, UK.
NeuroImage (Impact Factor: 6.36). 08/2005; 26(3):839-51. DOI: 10.1016/j.neuroimage.2005.02.018
Source: PubMed


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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    • "Preprocessing steps included bias-field correction and segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Segmented images were registered to standard Montreal Neurological Institute (MNI) space using the high-dimensional Dartel approach (Ashburner and Friston, 2005 "
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