Unified segmentation

被引：6632
作者：
Ashburner, J [1 ]
Friston, KJ [1 ]
机构：
[1] Wellcome Dept Imaging Neurosci, Funct Imaging Lab, London WC1N 3BG, England
来源：
NEUROIMAGE | 2005年 / 26卷 / 03期
基金：
英国惠康基金;
关键词：
linage registration; tissue classification; bias correction; mixture of Gaussians; tissue probability maps;
D O I：
10.1016/j.neuroimage.2005.02.018
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
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. (c) 2005 Elsevier Inc. All rights reserved.
引用
收藏
页码：839 / 851
页数：13
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