Description

Automated SEM dimple morphology quantification for ductile fracture studies. Python tool for extracting dimple diameters from SEM images using denoising, contrast enhancement, morphological filtering, and contour-based measurements with physical calibration.

Method Overview

1. Noise reduction: Median filtering is applied to suppress high-frequency SEM noise while preserving edge information.
2. Contrast enhancement: Contrast-limited adaptive histogram equalization (CLAHE) improves local contrast and reveals dimple boundaries.
3. Morphological filtering: Morphological opening with a disk-shaped structuring element removes isolated noise artifacts without altering larger void geometries.
4. Dimple identification: External contour detection is used to identify individual dimples.
5. Size quantification: Equivalent circular diameters
6. Scale calibration: Pixel-to-physical unit conversion is supported for different magnifications, enabling consistent cross-scale comparisons.

Files Included

1. process_SEM.py: Python image processing code
2. SEM_Images: Folder including characteristic SEM images that can directly be processed by process_SEM.py.

Reference

Skiadopoulos, A., and Lignos, D. G. (2026). “Ductile crack initiation in welded moment connections with simplified weld details and inelastic panel zones.” Journal of Constructional Steel Research, Elsevier (in press).