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J Struct Biol. 2010 Dec;172(3):233-43. doi: 10.1016/j.jsb.2010.06.019. Epub 2010 Jul 3.

An adaptive non-local means filter for denoising live-cell images and improving particle detection.

Author information

1
Department of Physics, Heriot-Watt University, Edinburgh EH14 4AS, UK. trilithy@gmail.com

Abstract

Fluorescence imaging of dynamical processes in live cells often results in a low signal-to-noise ratio. We present a novel feature-preserving non-local means approach to denoise such images to improve feature recovery and particle detection. The commonly used non-local means filter is not optimal for noisy biological images containing small features of interest because image noise prevents accurate determination of the correct coefficients for averaging, leading to over-smoothing and other artifacts. Our adaptive method addresses this problem by constructing a particle feature probability image, which is based on Haar-like feature extraction. The particle probability image is then used to improve the estimation of the correct coefficients for averaging. We show that this filter achieves higher peak signal-to-noise ratio in denoised images and has a greater capability in identifying weak particles when applied to synthetic data. We have applied this approach to live-cell images resulting in enhanced detection of end-binding-protein 1 foci on dynamically extending microtubules in photo-sensitive Drosophila tissues. We show that our feature-preserving non-local means filter can reduce the threshold of imaging conditions required to obtain meaningful data.

PMID:
20599512
PMCID:
PMC3087503
DOI:
10.1016/j.jsb.2010.06.019
[Indexed for MEDLINE]
Free PMC Article

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