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

DOI number:10.1016/j.micron.2018.09.002

Journal:Micron

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 FastFineCut can detect boundaries effectively and efficiently.

Co-author:Xiaojuan Ban,Ya Su,Chuni Liu,Hao Wang,Weihua Xue,Yonghong Zhi,Di Wu

First Author:Boyuan Ma

Indexed by:Journal paper

Discipline:Engineering

Document Type:J

Issue:116

Page Number:5-14

Translation or Not:no

Date of Publication:2019-01-01

Included Journals:SCI