Human-vision-inspired cluster identification for single-molecule localization microscopy

Single-molecule localization microscopy has enabled scientists to visualize cellular structures at the nanometer scale. However, researchers are facing great challenges in analyzing images presented by point clouds. Existing algorithms for cluster identification are coordinate-based analyses requiri...

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Bibliographic Details
Published in:Optics express 2023-01, Vol.31 (3), p.3459-3466
Main Authors: Chen, Lei, Liu, Qian, Chou, Keng C
Format: Article
Language:English
Subjects:
Algorithms
Humans
Microscopy - methods
Single Molecule Imaging
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Description
Summary:Single-molecule localization microscopy has enabled scientists to visualize cellular structures at the nanometer scale. However, researchers are facing great challenges in analyzing images presented by point clouds. Existing algorithms for cluster identification are coordinate-based analyses requiring users to input cutoff thresholds based on the distance or density of the point cloud. These thresholds are often one's best guess with repeated visual inspections, making the cluster assignment user-dependent. Here, we present a cluster identification algorithm mimicking the modulation transfer function of human vision. This approach does not require any input parameters and produces visually satisfactory cluster assignments. We tested this algorithm by identifying the clusters of the fusion proteins of the Nipah virus on its host cells. This algorithm was further extended to analyze three-dimensional point clouds using virus-like particles as an example.
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.476486