Accounting for Location Measurement Error in Imaging Data With Application to Atomic Resolution Images of Crystalline Materials
Accounting for Location Measurement Error in Imaging Data With Application to Atomic Resolution Images of Crystalline Materials
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DOI：
10.1080/00401706.2021.1905070
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发表时间：
2019-10
期刊：
影响因子：
2.5
通讯作者：
M. J. Miller;M. Cabral;E. Dickey;J. Lebeau;B. Reich
中科院分区：
文献类型：
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作者：
M. J. Miller;M. Cabral;E. Dickey;J. Lebeau;B. Reich
摘要科学家使用成像技术来识别感兴趣的物体，并推断这些物体的性质。这些物体的位置测量经常会有误差，当忽略这些误差时，会导致参数估计的偏差和夸大的方差。当前的测量误差方法需要测量误差方差的估计或知识来校正这些估计，而这可能是不可用的。相反，我们创建了一个空间贝叶斯分层模型，该模型将位置视为参数，使用图像本身来包含位置不确定性。我们通过在对象位置周围使用非邻接区块设计来近似似然来降低计算负担。我们利用这个模型量化了通过扫描电子显微镜(STEM)直接成像的晶体结构中数百个原子柱的强度与位移之间的关系。原子位移与重要的现象有关，如压电性，这是一种对超声波等工程应用有用的特性。量化这种关系的符号和大小将有助于材料科学家更准确地设计具有改进的压电性的材料。仿真研究证实，与非测量误差模型相比，我们的方法纠正了感兴趣参数估计中的偏差，并极大地提高了高噪声场景下的覆盖率。
Abstract Scientists use imaging to identify objects of interest and infer properties of these objects. The locations of these objects are often measured with error, which when ignored leads to biased parameter estimates and inflated variance. Current measurement error methods require an estimate or knowledge of the measurement error variance to correct these estimates, which may not be available. Instead, we create a spatial Bayesian hierarchical model that treats the locations as parameters, using the image itself to incorporate positional uncertainty. We lower the computational burden by approximating the likelihood using a noncontiguous block design around the object locations. We use this model to quantify the relationship between the intensity and displacement of hundreds of atom columns in crystal structures directly imaged via scanning transmission electron microscopy (STEM). Atomic displacements are related to important phenomena such as piezoelectricity, a property useful for engineering applications like ultrasound. Quantifying the sign and magnitude of this relationship will help materials scientists more precisely design materials with improved piezoelectricity. A simulation study confirms our method corrects bias in the estimate of the parameter of interest and drastically improves coverage in high noise scenarios compared to non-measurement error models.