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Micron. 2019 Jan;116:5-14. doi: 10.1016/j.micron.2018.09.002. Epub 2018 Sep 7.

Fast-FineCut: Grain boundary detection in microscopic images considering 3D information.

Author information

1
Beijing Advanced Innovation Center for Materials Genome Engineering, China; School of Computer and Communication Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China; Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China.
2
School of Materials Science and Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China.
3
School of Materials Science and Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China; School of Materials Science and Technology, Liaoning Technical University, China.
4
Mechanical and electrical design and research institute of Shanxi Province, Shengli Street 228, Xinghualing District, Taiyuan City, Shanxi Province 030009, China.
5
Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Aalesund, Norway.

Abstract

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 Fast-FineCut can detect boundaries effectively and efficiently.

KEYWORDS:

Grain boundary detection; Graph cut approaches; Microscopic images; Overlap-tile strategy; Polycrystalline iron

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