Analysis of video-based microscopic particle trajectories using Kalman filtering

Pei Hsun Wu, Ashutosh Agarwal, Henry Hess, Pramod P. Khargonekar, Yiider Tseng

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2822-2830
Number of pages9
JournalBiophysical journal
Volume98
Issue number12
DOIs
StatePublished - Jun 16 2010
Externally publishedYes

ASJC Scopus subject areas

  • Biophysics

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