.png){kind=icon}
返回
Single-pixel interior filling function approach for detecting and correcting errors in particle tracking
.png){kind=icon}
OA
AI
DOI：10.1073/pnas.1619104114.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a general method for detecting and correcting biases in the outputs of particle-tracking experiments. Our approach is based on the histogram of estimated positions within pixels, which we term the single-pixel interior filling function (SPIFF). We use the deviation of the SPIFF from a uniform distribution to test the veracity of tracking analyses from different algorithms. Unbiased SPIFFs correspond to uniform pixel filling, whereas biased ones exhibit pixel locking, in which the estimated particle positions concentrate toward the centers of pixels. Although pixel locking is a well-known phenomenon, we go beyond existing methods to show how the SPIFF can be used to correct errors. The key is that the SPIFF aggregates statistical information from many single-particle images and localizations that are gathered over time or across an ensemble, and this information augments the single-particle data. We explicitly consider two cases that give rise to significant errors in estimated particle locations: undersampling the point spread function due to small emitter size and intensity overlap of proximal objects. In these situations, we show how errors in positions can be corrected essentially completely with little added computational cost. Additional situations and applications to experimental data are explored in SI Appendix. In the presence of experimental- like shot noise, the precision of the SPIFF-based correction achieves (and can even exceed) the unbiased Cramer-Rao lower bound. We expect the SPIFF approach to be useful in a wide range of localization applications, including single-molecule imaging and particle tracking, in fields ranging from biology to materials science to astronomy.
Keyword:
imaging
particle tracking
error correction
pixel locking
Cramer-Rao lower bound
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
P
IF:
9.1
论文数:
10.8W
被引数:
73.5W
机构
引用论文
Baseline prevalence and predictors of liver fibrosis among HIV‐positive individuals: a substudy of the INSIGHT Strategic Timing of AntiRetroviral Treatment (START) trial.png)
HIV MedicineOAAI
HIV MedicineIF0
OAAI分享收藏分享收藏Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)通过随机光学重建显微镜 (STORM) 进行亚衍射极限成像NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏Fast, bias-free algorithm for tracking single particles with variable size and shapeOPTICS EXPRESS
OAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏
查看更多内容