Statistical methods in computational anatomy.
Statistical methods in computational anatomy.
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DOI：
10.1191/096228097673360480
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发表时间：
1997-09-01
影响因子：
2.3
通讯作者：
Matejic, L
中科院分区：
文献类型：
--
作者：
Miller, M;Banerjee, A;Matejic, L
本文回顾了华盛顿/布朗小组在计算解剖学这一新兴学科中解剖形状研究的最新进展。对计算解剖学中解剖变异的参数表示进行了回顾，仅限于小变形的假设。协方差算子的生成用于协调子流形上解剖变异的概率度量是一个经验过程。大脑种群被映射到共同的坐标系中，从这些坐标系中构造出在最小距离意义上最接近解剖学种群的模板坐标系。几种一维、二维和三维流形的变化，即脑沟、脑表面和脑容量，通过高斯测量进行检测，其平均值和协方差直接从模板到目标的映射中估计出来。给出了一组经验生成的映射中向量场协方差的估计方法，这些映射被设定为索引在子流形上的广义谱估计。协方差估计是参数化的，类似于自回归建模，通过引入小变形线性算子来约束场的频谱。
This paper reviews recent developments by the Washington/Brown groups for the study of anatomical shape in the emerging new discipline of computational anatomy. Parametric representations of anatomical variation for computational anatomy are reviewed, restricted to the assumption of small deformations. The generation of covariance operators for probabilistic measures of anatomical variation on coordinatized submanifolds is formulated as an empirical procedure. Populations of brains are mapped to common coordinate systems, from which template coordinate systems are constructed which are closest to the population of anatomies in a minimum distance sense. Variation of several one-, two- and three-dimensional manifolds, i.e. sulci, surfaces and brain volumes are examined via Gaussian measures with mean and covariances estimated directly from maps of templates to targets. Methods are presented for estimating the covariances of vector fields from a family of empirically generated maps, posed as generalized spectrum estimation indexed over the submanifolds. Covariance estimation is made parametric, analogous to autoregressive modelling, by introducing small deformation linear operators for constraining the spectrum of the fields.