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显微图像粒度分析技能skill-未经验证仅供尝试

Microscopy image particle size analysis and counting skill. Performs automated particle detection, equivalent diameter measurement, and size distribution analysis from brightfield, darkfield, or DIC microscopy images. Uses Otsu adaptive thresholding, morphological cleanup, watershed separation, and Rosin-Rammler distribution fitting. Outputs D10, D50, D90, qn and qv distribution coefficients, and shape parameters including circularity and aspect ratio per DIN 66141 and ISO 9276 standards. This skill should be used when analyzing particle size, counting particles in microscopy images, measuring powder or granule size distributions, or performing granulometric analysis.

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Microscopy Particle Size Analysis

Overview

Automated particle size analysis from microscopy images. The pipeline takes a high-resolution microscopy image (brightfield, darkfield, or DIC mode) and produces a complete granulometric report: binary mask, individual particle measurements, size distribution tables, cumulative curves (D10/D50/D90), and Rosin-Rammler fitting coefficients.

Three-stage pipeline:

  1. Image Preprocessing — Otsu binarization + morphological cleanup + watershed separation
  2. Particle Measurement — Equivalent diameter (Feret + Heywood weighted), shape parameters
  3. Distribution Analysis — DIN 66141 / ISO 9276 classification, D-values, Rosin-Rammler fit

When to Use This Skill

Trigger words: particle size analysis, granulometry, particle counting, microscopy image analysis, 粒度分析, 颗粒计数, 显微图像分析, 粒径分布, D50, D90, Rosin-Rammler, 粉体粒径, 颗粒形态

Applicable scenarios:

  • Analyzing powder/granule particle size from microscopy images
  • Counting and measuring particles in brightfield/darkfield/DIC images
  • Computing particle size distributions (D10, D50, D90, Span)
  • Fitting Rosin-Rammler distribution to particle data
  • Measuring particle shape parameters (circularity, aspect ratio, etc.)

Prerequisites

Python Environment

The analysis scripts require the following packages:

numpy>=1.21.0
opencv-python>=4.5.0
scipy>=1.7.0
matplotlib>=3.4.0  # optional, for chart generation

Install into the managed Python venv:

# Create venv if not exists
C:/Users/Administrator/.workbuddy/binaries/python/versions/3.13.12/python.exe -m venv C:/Users/Administrator/.workbuddy/binaries/python/envs/default

# Install dependencies
C:/Users/Administrator/.workbuddy/binaries/python/envs/default/Scripts/pip install numpy opencv-python scipy matplotlib

Scale Calibration Parameters

Before analysis, gather the following microscope configuration:

  • Eyepiece magnification (M1): typically 10x or 15x
  • Objective magnification (M2): e.g., 4x, 10x, 20x, 40x, 100x
  • Camera pixel size: physical sensor pixel size in micrometers (check camera spec sheet)

The scale factor is: S = pixel_size / (M1 * M2) [um/pixel]

Quick Start

Basic Analysis

# Default: brightfield, 10x eyepiece, 40x objective, 5.5um pixels
C:/Users/Administrator/.workbuddy/binaries/python/envs/default/python.exe \
    ~/.workbuddy/skills/microscopy-particle-analysis/scripts/particle_analyzer.py \
    particle_image.tif \
    --output-dir ./output

With Custom Parameters

C:/Users/Administrator/.workbuddy/binaries/python/envs/default/python.exe \
    ~/.workbuddy/skills/microscopy-particle-analysis/scripts/particle_analyzer.py \
    darkfield_sample.png \
    --mode darkfield \
    --eyepiece 10 --objective 20 --pixel-size 5.86 \
    --bins 0.5,1,2,5,10,20,50 \
    --charts --save-mask --save-annotated \
    --output-dir ./results

CLI Parameters

Required

| Parameter | Description | |-----------|-------------| | image | Path to microscopy image file (TIFF, PNG, JPG, BMP) |

Imaging Mode

| Parameter | Default | Description | |-----------|---------|-------------| | --mode | brightfield | Imaging mode: brightfield, darkfield, dic |

Scale Calibration

| Parameter | Default | Description | |-----------|---------|-------------| | --eyepiece | 10.0 | Eyepiece magnification M1 | | --objective | 40.0 | Objective magnification M2 | | --pixel-size | 5.5 | Camera pixel physical size (um) |

Equivalent Diameter Weighting

| Parameter | Default | Description | |-----------|---------|-------------| | --weight-feret | 0.5 | Weight for Feret diameter | | --weight-heywood | 0.5 | Weight for Heywood diameter |

Preprocessing

| Parameter | Default | Description | |-----------|---------|-------------| | --kernel-size | 3 | Morphological kernel size (odd) | | --opening | 2 | Opening iterations (noise removal) | | --closing | 2 | Closing iterations (hole filling) | | --no-watershed | off | Disable watershed separation | | --watershed-threshold | 0.5 | Watershed seed threshold (0-1) | | --min-area | 10 | Min particle area (pixels) | | --max-area | 500000 | Max particle area (pixels) | | --blur-size | 3 | Gaussian blur kernel (0=off) |

Distribution Analysis

| Parameter | Default | Description | |-----------|---------|-------------| | --standard | ISO9276 | Standard: DIN66141 or ISO9276 | | --bin-method | log | Bin spacing: log or arithmetic | | --bins | (default) | Custom bin boundaries, comma-separated (e.g., 0.5,1,2,5,10,20,50) |

Output

| Parameter | Default | Description | |-----------|---------|-------------| | --output-dir | ./output | Output directory | | --charts | off | Generate distribution charts | | --save-mask | off | Save binary mask image | | --save-annotated | off | Save annotated image with contours |

Output Files

The tool generates the following output files in the specified --output-dir:

1. {basename}_particles.csv

Individual particle data, one row per particle:

| Column | Unit | Description | |--------|------|-------------| | label | — | Connected component ID | | equivalent_diameter_um | um | Weighted equivalent diameter | | feret_diameter_um | um | Maximum Feret diameter | | min_feret_diameter_um | um | Minimum Feret diameter | | heywood_diameter_um | um | Area-equivalent circle diameter | | area_um2 | um^2 | Particle area | | perimeter_um | um | Contour perimeter | | aspect_ratio | — | Min Feret / Max Feret (0-1) | | circularity | — | 4piA / P^2 (0-1) | | convexity | — | Hull perimeter / contour perimeter (0-1) | | solidity | — | Area / hull area (0-1) | | elongation | — | 1 - aspect_ratio (0-1) | | centroid_x, centroid_y | pixels | Particle centroid position |

2. {basename}_report.json

Complete analysis report in JSON format, including:

  • Image metadata and scale configuration
  • Preprocessing parameters and particle counts
  • Full distribution analysis results (bins, D-values, Span, Rosin-Rammler)
  • Summary statistics (mean, geometric mean, harmonic mean, std dev)

3. {basename}_distribution_charts.png (with --charts)

Distribution charts:

  • Top: Number (qn) vs Volume (qv) distribution bar chart
  • Bottom: Cumulative volume distribution curve with D10/D50/D90 markers

4. {basename}_mask.png (with --save-mask)

Binary mask image (0=background, 255=particle).

5. {basename}_annotated.png (with --save-annotated)

Original image with green particle contours and scale bar overlay.

Workflow

When processing a microscopy image for particle analysis:

  1. Gather microscope configuration — determine eyepiece/objective magnification and camera pixel size.

  2. Install dependencies — ensure numpy, opencv-python, scipy are installed in the Python environment.

  3. Run the analyzer — execute particle_analyzer.py with appropriate parameters:

    • Set --mode based on imaging technique
    • Set --eyepiece, --objective, --pixel-size for accurate physical measurements
    • Adjust --bins if custom size ranges are needed
    • Enable --charts for visual output

  4. Review results — check the console summary, CSV data, and JSON report. Verify:

    • Particle count is reasonable (not 0, not excessively high)
    • D50 value is in expected range for the sample
    • Rosin-Rammler R² > 0.8 indicates good fit

  5. Adjust parameters if needed — if results are unsatisfactory:

    • No particles detected → try --mode darkfield, lower --min-area, or --no-watershed
    • Over-segmentation → increase --watershed-threshold (e.g., 0.7)
    • Under-segmentation → decrease --watershed-threshold (e.g., 0.3)
    • Too many noise particles → increase --min-area or --opening

Algorithm Details

For detailed technical documentation on the algorithms used, refer to:

  • references/standards_reference.md — DIN 66141 / ISO 9276 standards, equivalent diameter methods, Rosin-Rammler fitting parameters
  • references/methodology.md — Otsu algorithm, morphological operations, watershed segmentation, Feret diameter calculation, Rosin-Rammler linearization

Script Architecture

scripts/
├── particle_analyzer.py      # Main CLI entry point - orchestrates the full pipeline
├── image_preprocessing.py    # Image loading, Otsu binarization, morphology, watershed
├── particle_measurement.py   # Connected components, Feret/Heywood diameter, shape parameters
├── distribution_analysis.py  # Size classification, D-values, Rosin-Rammler fitting
└── requirements.txt          # Python dependencies

Limitations

  • Volume estimation: Particle volume is approximated as equivalent sphere (π/6 * D³). For highly non-spherical particles, this introduces bias.
  • Watershed separation: Cannot perfectly separate heavily overlapping particles. Very tight clusters may still be counted as single particles.
  • Otsu thresholding: Performs poorly on images with extreme non-uniform illumination. Consider pre-processing with CLAHE in such cases.
  • Rosin-Rammler fit: Best suited for ground/milled powders. Bimodal or log-normal distributions may show poor R².
  • Minimum particle size: Particles smaller than ~3 pixels in diameter cannot be reliably measured. Use higher magnification for sub-micron particles.