Single-pixel interior filling function approach for detecting and correcting errors in particle tracking

Stanislav Burov, Patrick Figliozzi, Binhua Lin, Stuart A. Rice, Norbert F. Scherer, Aaron R. Dinner

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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 Cramér-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.

Original languageEnglish
Pages (from-to)221-226
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume114
Issue number2
DOIs
StatePublished - 10 Jan 2017

Funding

FundersFunder number
National Science Foundation1346572

    Keywords

    • Cramér-Rao lower bound
    • Error correction
    • Imaging
    • Particle tracking
    • Pixel locking

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