Department of Chemical Engineering, University of Florida, Gainesville, Florida, USA.
Biophys J. 2010 Jun 16;98(12):2822-30. doi: 10.1016/j.bpj.2010.03.020.
The fidelity of the trajectories obtained from video-based particle tracking determines the success of a variety of biophysical techniques, including in situ single cell particle tracking and in vitro motility assays. However, the image acquisition process is complicated by system noise, which causes positioning error in the trajectories derived from image analysis. Here, we explore the possibility of reducing the positioning error by the application of a Kalman filter, a powerful algorithm to estimate the state of a linear dynamic system from noisy measurements. We show that the optimal Kalman filter parameters can be determined in an appropriate experimental setting, and that the Kalman filter can markedly reduce the positioning error while retaining the intrinsic fluctuations of the dynamic process. We believe the Kalman filter can potentially serve as a powerful tool to infer a trajectory of ultra-high fidelity from noisy images, revealing the details of dynamic cellular processes.
基于视频的粒子追踪获得的轨迹的准确性决定了各种生物物理技术的成功，包括原位单细胞粒子追踪和体外运动分析。然而，图像采集过程受到系统噪声的影响，这会导致从图像分析中得出的轨迹的定位误差。在这里，我们通过应用卡尔曼滤波器来探索减少定位误差的可能性，卡尔曼滤波器是一种从噪声测量中估计线性动态系统状态的强大算法。我们表明，可以在适当的实验设置中确定最佳卡尔曼滤波器参数，并且卡尔曼滤波器可以显著减少定位误差，同时保留动态过程的固有波动。我们相信卡尔曼滤波器有可能成为从噪声图像中推断超高精度轨迹的有力工具，从而揭示动态细胞过程的细节。
