Grain Segmentation of 3D Superalloy Images Using Multichannel EWCVT under Human Annotation Constraints

Grain segmentation on 3D superalloy images provides superalloy’s micro-structures, based on which many physical and mechanical properties can be evaluated. This is a challenging problem in senses of (1) the number of grains in a superalloy sample could be thousands or even more; (2) the intensity within a grain may not be homogeneous; and (3) superalloy images usually contains carbides and noises. Recently, the Multichannel Edge-Weighted Centroid Voronoi Tessellation (MCEWCVT) algorithm [1] was developed for grain segmentation and showed better performance than many widely used image segmentation algorithms. However, as a general-purpose clustering algorithm, MCEWCVT does not consider possible prior knowledge from material scientists in the process of grain segmentation. In this paper, we address this issue by defining an energy minimization problem which subject to certain constraints. Then we develop a Constrained Multichannel Edge-Weighted Centroid Voronoi Tessellation (CMEWCVT) algorithm to effectively solve this constrained minimization problem. In particular, manually annotated segmentation on a very small set of 2D slices are taken as constraints and incorporated into the whole clustering process. Experimental results demonstrate that the proposed CMEWCVT algorithm significantly improve the previous grain-segmentation performance.