Ashburner John, Friston Karl J
Wellcome Department of Imaging Neuroscience, 12 Queen Square, London, WC1N 3BG, UK.
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.
提出了一个概率框架，该框架能够在同一生成模型中结合图像配准、组织分类和偏差校正。给出了统一模型对数似然目标函数的推导。该模型基于高斯混合模型，并进行了扩展，以纳入平滑强度变化和带有组织概率图的非线性配准。描述了优化模型参数的策略以及目标函数所需的偏导数。
