Troubleshooting Common ijGranulometry Errors and Solutions

Top 7 Tips for Optimizing ijGranulometry WorkflowsijGranulometry is a powerful ImageJ/Fiji plugin suite for particle and grain size analysis used in materials science, geology, biology and industrial quality control. Optimizing workflows in ijGranulometry increases accuracy, repeatability, and throughput. Below are seven practical, field-tested tips to streamline your analysis, reduce user bias, and get reliable granulometric data faster.

1. Start with consistent image acquisition

Image quality is the single biggest determinant of analysis success.

• Use stable, calibrated illumination: uneven lighting creates false edges and size biases. Prefer Köhler illumination or diffuse LED panels.
• Fix magnification and resolution: keep the same objective and pixel scale across samples. Record pixel-to-micron calibration for each magnification.
• Minimize noise and blur: use appropriate exposure/time and, if possible, capture multiple frames and average to reduce shot noise.
• Include a scale bar or calibration target in each session to detect drift and ensure correct physical units.

Why it matters: consistent acquisition reduces preprocessing work and improves repeatability.

2. Preprocess images thoughtfully

Preprocessing prepares images for robust segmentation and granulometry measurements.

• Background correction: use flat-field correction or rolling-ball background subtraction to remove gradients.
• Denoising: apply non-destructive denoising like a median or non-local means filter. Avoid aggressive smoothing that alters particle edges.
• Contrast enhancement: apply histogram equalization or CLAHE for low-contrast images, but use consistent parameters across the dataset.
• Channel selection and color conversion: if using RGB images, select the most informative channel or convert to grayscale using a consistent method.

Practical pipeline example in Fiji: flat-field correction → median filter (radius 1–2 px) → CLAHE (blocksize 127, histogram bins 256) → 8-bit conversion.

3. Choose robust segmentation strategies

Segmentation quality directly affects area and shape measurements.

• Thresholding: use automated methods (Otsu, Li, Yen) but validate versus manual thresholds on representative images. For inhomogeneous images, apply local (adaptive) thresholding.
• Watershed and separation: use distance transform + watershed to split touching particles; consider marker-controlled watershed when over-segmentation is an issue.
• Morphological operations: apply small opening/closing to remove speckle noise or to clean object boundaries, but avoid changing true particle sizes.
• Machine learning segmentation: for complex textures, train ilastik or WEKA classifiers in Fiji to improve object delineation. Save classifiers for batch processing.

Tip: always visually inspect segmentation results on a subset and compute simple statistics (mean area, count) before full batch runs.

4. Configure ijGranulometry parameters for your sample type

ijGranulometry offers many measurement and fitting options; match them to your materials.

• Granulometric kernels and structuring elements: choose disk/ball kernels that reflect particle shapes (circular vs. elongated). Kernel size range should span from the smallest detectable feature up to ~50% of largest particle.
• Measurement modes: for size distributions use area-equivalent diameter or Feret diameters depending on analysis needs. Report which metric you used.
• Cumulative and differential granulometry: extract both to view distribution shape and median/percentile sizes (D10, D50, D90).
• Noise filters and minimum particle size: set a minimum area threshold based on resolution to exclude artifacts; document this threshold.

Recommendation: run parameter sweeps on a representative image to see how results shift, then lock parameters for the full dataset.

5. Automate batch processing with reproducibility in mind

Automation saves time and reduces human error — but must be reproducible.

• Macro scripting: write Fiji/ImageJ macros to standardize sequence (preprocess → segment → run ijGranulometry → export).
• Use saved parameter files and classifiers: keep parameter sets and WEKA/ilastik models under version control with timestamps.
• Logging and metadata: export processing logs, parameter values, and image metadata with results. Include pixel calibration, threshold values, and filter sizes.
• Parallelize cautiously: if processing many images, use Fiji headless mode or a cluster, but ensure memory limits and reproducibility across nodes.

Best practice: include an example image and its expected output in your project so others can verify the pipeline.

6. Validate results and quantify uncertainty

Don’t assume outputs are correct — validate them.

• Ground truth comparison: where possible, compare automated counts/sizes to hand-segmented ground truth on a test set and compute metrics (precision, recall, IoU).
• Statistical checks: compute repeatability by reprocessing images or processing replicate samples; report standard deviations or confidence intervals for key metrics (e.g., D50 ± SD).
• Sensitivity analysis: vary key preprocessing/segmentation parameters and observe effects on D-values and shape descriptors to quantify robustness.
• Outlier detection: inspect extreme measurements and use morphological filters or manual curation for obvious segmentation failures.

Illustration: if D50 changes by >5–10% with small threshold shifts, report that uncertainty or refine segmentation.

7. Report results clearly and store data for reuse

Good reporting enables interpretation, comparison, and reanalysis.

• Document methods: include image acquisition settings, preprocessing steps and parameter values, segmentation approach, ijGranulometry kernel settings, and thresholds.
• Provide both distribution plots and summary metrics: cumulative distribution, histogram, D10/D50/D90, mean, skewness, and number concentration.
• Use standardized file formats: export CSV for measurements, PNG/SVG for figures, and TIFF for processed images. Bundle raw images, processed masks, and analysis scripts.
• Metadata and provenance: attach a README or JSON metadata file describing dataset, calibration, and software versions (ImageJ/Fiji version, ijGranulometry version, plugins used).

Consider publishing a small sample dataset and the Fiji macro used so reviewers or colleagues can reproduce your results.

Quick checklist (one-line)

• Calibrate and standardize imaging.
• Apply consistent, minimal preprocessing.
• Validate segmentation and use adaptive or ML methods if needed.
• Tune ijGranulometry kernels and thresholds for your sample.
• Automate with macros and log parameters.
• Validate against ground truth and quantify uncertainty.
• Document, export, and archive raw, processed, and result files.

Optimizing ijGranulometry workflows is about reducing variability and making analysis reproducible. Focus on acquisition consistency, validated segmentation, careful parameter selection, and transparent reporting to produce robust granulometric data.