AI achieves separation of overlapping particles once difficult with classical image processing. No tuning required—consistent, high-accuracy results that improve the reliability of particle size distributions.
| Comparison Item | Traditional Image Analysis |  Morfolia |
|---|---|---|
| Approach | Binarization + Watershed | Deep Learning Model |
| Overlapping Particle Detection | Difficult separation / frequent misdetections | High-precision separation even in dense contacts |
| Setup Effort | Manual tuning per image | No setup (auto-optimized) |
| Result Consistency | Operator- and condition-dependent | Stable across varying conditions |
| Accuracy | Varies widely by conditions | High accuracy and reproducibility |
| Ease of Use | Requires expertise | One-click analysis |
Results vary greatly depending on threshold settings. Susceptible to uneven lighting and shadows, making accurate particle contour extraction difficult.
Prone to over-segmentation (one particle split into multiple) and under-segmentation (multiple particles recognized as one), particularly reducing separation accuracy for overlapping particles.
Need to find optimal parameters for each image, dependent on operator experience and skill. Time-consuming with reduced reproducibility.
Trained on diverse microscopic images, the AI recognizes particles automatically. Delivers the same accuracy without parameter tuning.
Learns overlap patterns to individually identify densely packed particles, improving the reliability of size distributions.
Robust to lighting and image quality variations. Provides consistent results that strengthen quality control.
Results vary greatly depending on threshold settings. Susceptible to uneven lighting and shadows, making accurate particle contour extraction difficult.
Trained on diverse microscopic images, the AI recognizes particles automatically. Delivers the same accuracy without parameter tuning.
Prone to over-segmentation (one particle split into multiple) and under-segmentation (multiple particles recognized as one), particularly reducing separation accuracy for overlapping particles.
Learns overlap patterns to individually identify densely packed particles, improving the reliability of size distributions.
Need to find optimal parameters for each image, dependent on operator experience and skill. Time-consuming with reduced reproducibility.
Robust to lighting and image quality variations. Provides consistent results that strengthen quality control.
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