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Cluster parameter-based DBSCAN maps for image characterization
Péter Bíró1, Bálint Barna H Kovács1, Tibor Novák1
1Department of Optics and Quantum Electronics, University of Szeged, Dóm tér 9, Szeged, 6720, Hungary.
Computational and Structural Biotechnology Journal
|March 24, 2025
Summary
We developed cluster parameter-based DBSCAN maps for optimizing single-molecule localization microscopy (SMLM) analysis. These maps enable direct, sensitive parameter selection for accurate nanocluster visualization and characterization in biological imaging.
Area of Science:
- Biophysics
- Microscopy
- Computational Biology
Background:
- Single-molecule localization microscopy (SMLM) enables visualization of nanoscale biological structures.
- Quantitative analysis of SMLM data often relies on clustering algorithms like DBSCAN.
- DBSCAN performance is highly sensitive to parameter selection, necessitating optimization strategies.
Purpose of the Study:
- To introduce novel cluster parameter-based DBSCAN maps for SMLM data analysis.
- To provide a direct method for parameter optimization and image characterization in SMLM.
- To assess the utility of these maps for sensitivity studies and comparison with existing methods.
Main Methods:
- Development of cluster parameter-based DBSCAN maps.
- Application of maps to simulated and experimentally measured SMLM datasets.
- Comparative analysis with lacunarity analysis for SMLM data.
Main Results:
- Demonstrated direct applicability of DBSCAN maps to measured SMLM datasets.
- Showcased the utility of maps for image characterization and parameter optimization.
- Validated the effectiveness of the proposed method against lacunarity analysis.
Conclusions:
- Cluster parameter-based DBSCAN maps offer a robust tool for SMLM data analysis.
- These maps facilitate accurate nanocluster visualization and quantitative evaluation.
- The developed method enhances the reliability and efficiency of SMLM data interpretation.
