WC electron microscopy image segmentation based on improved watershed and Hu-moment edge matching algorithms

• Published in: Computational materials science Vol. 246; p. 113401
• Main Authors: Zhong, Yixuan, Liu, Yi, Liu, Kai, Zhan, Teng, Liu, Shuli, Liang, Yunlong, Hu, Yuliang, Li, Mingfu, Lei, Gaopan, Zhou, Shiyu, Liu, Jingang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2025
• Subjects: Adhesion particles; Image segmentation; Particle size distribution; Regional mergers; WC particles; Adhesion particles; WC particles; Image segmentation; Particle size distribution; Regional mergers
• ISSN: 0927-0256
• DOI: 10.1016/j.commatsci.2024.113401

[Display omitted] •Proposes a method combining improved watershed and Hu-moment edge matching for accurate WC powderparticle size analysis.•This method enables accurate segmentation and particle size analysis of adherent particles in WC electronmicroscope images.•Experimental results show particle size standard deviations under 3%, improving accuracy significantly over other methods. The particle size distribution of WC powder particles has a great influence on material properties. However, the traditional manual particle size analysis methods are both time-consuming and inaccurate, and the commonly used particle size detection methods belong to statistical indexes, which cannot reflect the real particle size. To address the above problems, this paper proposes an image segmentation method based on the improved watershed algorithm and the Hu-moment edge matching algorithm, which can realize accurate segmentation and particle size analysis of adherent particles in WC electron microscope images. First, an improved bilateral filtering and Otsu image coarse segmentation method is proposed to extract the target region of particles; then, an improved watershed algorithm based on the multi-threshold H-maxima transform is proposed to realize the segmentation of adherent particles; and a region merging correction based on the Hu-moment edge matching algorithm is proposed to avoid over-segmentation. We compare and analyze the performance of this method with manual segmentation and some other common segmentation methods. The experimental results show that the standard deviations of the particle sizes obtained by the method proposed in this paper are less than 3%, and the segmentation accuracy is greatly improved compared with other segmentation algorithms.