Particle Size (PSA) (EM)
Particle size analysis is essential across industries such as chemical, food, mining, forestry, agriculture, cosmetics, pharmaceuticals, energy, and aggregates. Accurate analysis depends on reliable particle segmentation from the background and, importantly, on precise separation of touching or clustered particles.
The Image-Pro Particle Size (PSA)(EM) protocol simplifies this process with easily configurable steps, enabling efficient analysis of large datasets with minimal image analysis expertise. Particles can be segmented using a pre-trained deep learning model, machine learning, or threshold segmentation—ensuring flexibility and precision for diverse applications.
Techniques: SEM, TEM
How it works
Select Channel
Select the channel that contains particles.
Set Diameter
Set the diameter for a pre-trained deep learning model, or set machine learning or threshold segmentation settings.
Find Particles
Automatically find particles.
Quantitative results
Automatically generate tables, heat maps, charts and even complex bespoke reports.
Measurement parameters supported
- • Number of Fields Analyzed
- • Total Particle Count
- • Diameter (Mean)
- • D10
- • D50
- • D90
- • Custom user-defined measurements
Solution requirements
Required Modules
Base
2D Automated Analysis
Materials EM Protocol Collection
Particle Size (EM) Protocol
AI Deep Learning
Materials Models
Versatile Particles Model
Recommended Package
Literature spotlight
- Lee, J., Jeong, H., & Ma, S. (2022). Effects of annealing temperature on structural phase transition and microstructure evolution of hydrothermally synthesized barium titanate nanoparticles. Materials Research Express, 9(6), 065001.
- Ma, J., Zhang, H., Wang, D., Wang, H., & Chen, G. (2022). Rheological properties of cement paste containing ground fly ash based on particle morphology analysis. Crystals, 12(4), 524.
- Quilaqueo, M., Gim-Krumm, M., Ruby-Figueroa, R., Troncoso, E., & Estay, H. (2019). Determination of size distribution of precipitation aggregates using non-invasive microscopy and semiautomated image processing and analysis. Minerals, 9(12), 724.