Yifat Yuval, Sule Nishant, Lin Yihan, Scherer Norbert F
James Franck Institute, The University of Chicago, Chicago Il, 60637, USA.
Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.
Sci Rep. 2017 Nov 29;7(1):16553. doi: 10.1038/s41598-017-14166-6.
Particle tracking, which is an essential tool in many fields of scientific research, uses algorithms that retrieve the centroid of tracked particles with sub-pixel accuracy. However, images in which the particles occupy a small number of pixels on the detector, are in close proximity to other particles or suffer from background noise, show a systematic error in which the particle sub-pixel positions are biased towards the center of the pixel. This "pixel locking" effect greatly reduces particle tracking accuracy. In this report, we demonstrate the severity of these errors by tracking experimental (and simulated) imaging data of optically trapped silver nanoparticles and single fluorescent proteins. We show that errors in interparticle separation, angle and mean square displacement are significantly reduced by applying the corrective Single-Pixel Interior Filling Function (SPIFF) algorithm. Our work demonstrates the potential ubiquity of such errors and the general applicability of SPIFF correction to many experimental fields.
粒子追踪是许多科研领域的重要工具，它使用算法以亚像素精度检索被追踪粒子的质心。然而，当粒子在探测器上占据的像素数量较少、与其他粒子靠得很近或受到背景噪声影响时，图像会显示出一种系统误差，即粒子的亚像素位置会偏向像素中心。这种“像素锁定”效应大大降低了粒子追踪的准确性。在本报告中，我们通过追踪光学捕获的银纳米颗粒和单个荧光蛋白的实验（和模拟）成像数据，证明了这些误差的严重性。我们表明，应用校正性的单像素内部填充函数（SPIFF）算法可显著减少粒子间距离、角度和均方位移的误差。我们的工作证明了此类误差可能普遍存在，以及SPIFF校正对许多实验领域的普遍适用性。
