Unified segmentation

Neuroimage. 2005 Jul 1;26(3):839-51. doi: 10.1016/j.neuroimage.2005.02.018. Epub 2005 Apr 1.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

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