Edge-Weighted Centroid Voronoi Tessellation with Propagation of Consistency Constraint for 3D Grain Segmentation in Microscopic Superalloy Images

Abstract

Abstract-3D microstructures are important for material scientists to analyze physical properties of materials. While such microstructures are too small to be directly visible to human vision, modern microscopic and serial-sectioning techniques can provide their high-resolution 3D images in the form of a sequence of 2D image slices. In this paper, we propose an algorithm based on the Edge-Weighted Centroid Voronoi Tessellation which uses propagation of the inter-slice consistency constraint. It can segment a 3D superalloy image, slice by slice, to obtain the underlying grain microstructures. With the propagation of the consistency constraint, the proposed method can automatically match grain segments between slices. On each of the 2D image slices, stable structures identified from the previous slice can be well-preserved, with further refinement by clustering the pixels in terms of both intensity and spatial information. We tested the proposed algorithm on a 3D superalloy image consisting of 170 2D slices. Performance is evaluated against manually annotated groundtruth segmentation. The results show that the proposed method outperforms several state-of-the-art 2D, 3D, and propagationbased segmentation methods in terms of both segmentation accuracy and running time.

Saliency Detection with VORONOI Diagram

“…The skeleton of the free space in the local surround is found by a real-time calculated Voronoi graph. An algorithm based on the edge-weighted centroid Voronoi tessellation which uses propagation of the inter-slice consistency constraint is presented in [20]. It can segment a 3D superalloy image, slice by slice, to obtain the underlying grain microstructures.…”