Zhang Jiawei, Li Chen, Rahaman Md Mamunur, Yao Yudong, Ma Pingli, Zhang Jinghua, Zhao Xin, Jiang Tao, Grzegorzek Marcin
Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169 China.
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052 Australia.
Arch Comput Methods Eng. 2023;30(1):639-673. doi: 10.1007/s11831-022-09811-x. Epub 2022 Sep 6.
With the acceleration of urbanization and living standards, microorganisms play an increasingly important role in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image analysis methods with time. This study has high research significance and application value, which can be referred to as microbial researchers to comprehensively understand microorganism biovolume measurements using digital image analysis methods and potential applications.
随着城市化进程的加速和生活水平的提高，微生物在工业生产、生物技术和食品安全检测中发挥着越来越重要的作用。微生物生物体积测量是微生物分析的重要组成部分之一。然而，传统的手动测量方法耗时且难以精确测量其特征。随着数字图像处理技术的发展，微生物群体的特征可以被检测和量化。微生物生物体积测量方法的应用自20世纪80年代以来不断发展。本研究综述了62篇以上的文章，并根据数字图像分析方法按时间对这些文章进行了分组。本研究具有较高的研究意义和应用价值，可供微生物研究人员参考，以全面了解使用数字图像分析方法进行的微生物生物体积测量及其潜在应用。
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