Most Likely Separation of Intensity and Warping Effects in Image Registration
Most Likely Separation of Intensity and Warping Effects in Image Registration
复制标题
图像配准中强度和扭曲效应最有可能的分离
DOI：
10.1137/16m1070980
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
L. L. Rakêt
中科院分区：
文献类型：
--
作者：
Line Kühnel;S. Sommer;A. Pai;L. L. Rakêt
本文介绍了一类混合效应模型，用于二维（2D）图像中空间相关的亮度变化和翘曲变化的联合建模。空间相关的强度变化和翘曲变化被建模为随机效应，从而产生非线性混合效应模型，该模型能够通过优化似然函数来同时估计模板和模型参数。我们提出了一个算法来拟合模型，交替估计方差参数和图像配准。这种方法避免了在将配准作为预处理步骤时出现的模板估计中的潜在估计偏差。我们将该模型应用于面部图像和二维脑磁共振图像的数据集，以说明强度和翘曲效应的同时估计和预测。
This paper introduces a class of mixed-effects models for joint modeling of spatially correlated intensity variation and warping variation in two-dimensional (2D) images. Spatially correlated intensity variation and warp variation are modeled as random effects, resulting in a nonlinear mixed-effects model that enables simultaneous estimation of template and model parameters by optimization of the likelihood function. We propose an algorithm for fitting the model which alternates estimation of variance parameters and image registration. This approach avoids the potential estimation bias in the template estimate that arises when treating registration as a preprocessing step. We apply the model to datasets of facial images and 2D brain magnetic resonance images to illustrate the simultaneous estimation and prediction of intensity and warp effects.
影响因子：
5.8
作者：
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者：
Riddell, Allen
影响因子：
1.9
作者：
LINDSTROM, MJ;BATES, DM
通讯作者：
BATES, DM