Journal
JOURNAL OF STRUCTURAL BIOLOGY
Volume 172, Issue 3, Pages 233-243Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jsb.2010.06.019
Keywords
Denoising; Particle detection; Feature extraction; Non-local means filter; Drosophila; Microtubules
Funding
- Engineering and Physical Sciences Research Council, UK [EP/F018673/1]
- Wellcome Trust [081858]
- EPSRC [EP/F018673/1, EP/F019165/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/F018673/1, EP/F019165/1] Funding Source: researchfish
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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. (C) 2010 Elsevier Inc. All rights reserved.
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