Grain-oriented segmentation of scanning electron microscope images

Abstract

Quantitative analysis of nanostructures from scanning electron microscope (SEM) images requires a clear segmentation of grains and their boundaries. This is not provided by active contour models, which also require user guidance. Our automatic technique creates a rough representation of grain boundaries by adaptive thresholding. It then performs raycasting from a rectangular grid of seed points to ensure that the grain shapes are convex, and selects the best result for each grain. The whole process can be repeated several times to improve the segmentation. We present results for images of titanium foil, which show that our approach compares favorably in terms of speed and segmentation quality with four competing techniques.

Grain‐oriented segmentation of images of porous structures using ray casting and curvature energy minimization

“…Some seeds will produce distorted grain shapes because they are located on or near the edge of a grain. In order to select the best candidate grains, we define roundness as the round level of a grain contour and then we perform a simple roundness measurement (Lee et al, 2013) as follows:…”

“…Accuracy of grain identification in six SEM images of the titanium oxide foil: (A) different values of the normalization parameter λ, (B) different numbers of casting rays N ray , (C) different sample intervals h and (D) different values of the discard parameter θ R , comparing the proposed method (GOS-C) with a previous technique (GOS-R)(Lee et al, 2013).…”