Grain Boundary Detection and Phase Segmentation of SEM Ferrite–Pearlite Microstructure Using SLIC and Skeletonization

In this paper, we report an efficient segmentation and grain boundary detection process using modern image processing operators like simple linear iterative clustering and skeletonization. Accurate phase segmentation is the major requirement for any phase identification and quantification operations. The proposed image processing methods have been experimented on the in-house generated 48 scanning electron microscopy (SEM) microstructures obtained from plain carbon steel samples containing 0.1, 0.22, 0.35 and 0.48 wt%C and have been subjected to both annealing and normalizing treatments. The microstructures for dataset have been captured in SEM using secondary electron mode over a wide range of magnification × 500–× 5000. The experimental results significantly validate the segmentation of ferrite and pearlite regions. Also, the grain boundary detection results appear to be plausibly effective in case of ferrite–ferrite and ferrite–pearlite boundaries. However, the grain boundary detection efficiency is found to be relatively poor in case of pearlite–pearlite boundary. The overall performance of the proposed image processing technique in context of ferrite–pearlite steel SEM images shows promising results in all circumstances of compositional range, heat treatment and magnification.