Unified segmentation

被引：6593

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

页码：839 / 851

页数：13

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