ArticleCells2022
Application of Lacunarity for Quantification of Single Molecule Localization Microscopy Images.
Article in Cells, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Cluster parameter-based DBSCAN maps for image characterization.Computational and structural biotechnology journal · 2025Article
- Analysing the surface morphology of annealed FTO/ZnS bilayer films: stereometric, fractal, and wettability approaches.Scientific reports · 2024Article
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Authors and funding
9 authors.
Funding
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Abstract
The quantitative analysis of datasets achieved by single molecule localization microscopy is vital for studying the structure of subcellular organizations. Cluster analysis has emerged as a multi-faceted tool in the structural analysis of localization datasets. However, the results it produces greatly depend on the set parameters, and the process can be computationally intensive. Here we present a new approach for structural analysis using lacunarity. Unlike cluster analysis, lacunarity can be calculated quickly while providing definitive information about the structure of the localizations. Using simulated data, we demonstrate how lacunarity results can be interpreted. We use these interpretations to compare our lacunarity analysis with our previous cluster analysis-based results in the field of DNA repair, showing the new algorithm's efficiency.
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Registered trials
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