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DOI码：10.1016/j.micron.2018.09.002

发表刊物：Micron

摘要：The inner structure of a material is called its microstructure. It stores the genesis of a material and determines all the physical and chemical properties. However, the microstructure is highly complex and numerous image defects such as vague or missing boundaries formed during sample preparation, which makes it difficult to extract the grain boundaries precisely. In this work, we address the task of grain boundary detection in microscopic image processing and develop a graph-cut based method called Fast-FineCut to solve the problem. Our algorithm makes two key contributions: (1) An improved approach that incorporates 3D information between slices as domain knowledge, which can detect the boundaries precisely, even for the vague and missing boundaries. (2) A local processing method based on overlap-tile strategy, which can not only solve the “chain scission” problem at the edge of images, but also economize on the consumption of computing resources. We conduct experiments on a stack of 296 slices of microscopic images of polycrystalline iron (1600 × 2800) and compare the performance against several state-of-the-art boundary detection methods. We conclude that FastFineCut can detect boundaries effectively and efficiently.

合写作者：Xiaojuan Ban,Ya Su,Chuni Liu,Hao Wang,Weihua Xue,Yonghong Zhi,Di Wu

第一作者：Boyuan Ma

论文类型：期刊论文

学科门类：工学

文献类型：J

期号：116

页面范围：5-14

是否译文：否

发表时间：2019-01-01

收录刊物：SCI