Ruszczycki B, Bernas T. Quality of biological images, reconstructed using localization microscopy data.
Bioinformatics 2018;
34:845-852. [PMID:
29028905 PMCID:
PMC6192211 DOI:
10.1093/bioinformatics/btx597]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 09/22/2017] [Indexed: 11/26/2022] Open
Abstract
Motivation
Fluorescence localization microscopy is extensively used to study the details of
spatial architecture of subcellular compartments. This modality relies on determination
of spatial positions of fluorophores, labeling an extended biological structure, with
precision exceeding the diffraction limit. Several established models describe influence
of pixel size, signal-to-noise ratio and optical resolution on the localization
precision. The labeling density has been also recognized as important factor affecting
reconstruction fidelity of the imaged biological structure. However, quantitative data
on combined influence of sampling and localization errors on the fidelity of
reconstruction are scarce. It should be noted that processing localization microscopy
data is similar to reconstruction of a continuous (extended) non-periodic signal from a
non-uniform, noisy point samples. In two dimensions the problem may be formulated within
the framework of matrix completion. However, no systematic approach has been adopted in
microscopy, where images are typically rendered by representing localized molecules with
Gaussian distributions (widths determined by localization precision).
Results
We analyze the process of two-dimensional reconstruction of extended biological
structures as a function of the density of registered emitters, localization precision
and the area occupied by the rendered localized molecule. We quantify overall
reconstruction fidelity with different established image similarity measures.
Furthermore, we analyze the recovered similarity measure in the frequency space for
different reconstruction protocols. We compare the cut-off frequency to the limiting
sampling frequency, as determined by labeling density.
Availability and implementation
The source code used in the simulations along with test images is available at
https://github.com/blazi13/qbioimages.
Supplementary information
Supplementary data are
available at Bioinformatics online.
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