A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

Overview

Journal Arch Comput Methods Eng

Date 2022 Sep 12

PMID 36091717

Authors

Jiawei Zhang

Chen Li

Md Mamunur Rahaman

Yudong Yao

Pingli Ma

Jinghua Zhang

Xin Zhao

Tao Jiang

Marcin Grzegorzek

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Increased basal fibronectin is sufficient to promote excess endothelial cell matrix assembly causing localized barrier dysfunction.

Resnikoff H, Schwarzbauer J Mol Biol Cell. 2024; 35(9):ar120.

PMID: 39046775 PMC: 11449387. DOI: 10.1091/mbc.E24-02-0090.

EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation.

Yang H, Li C, Zhao X, Cai B, Zhang J, Ma P Front Microbiol. 2023; 14:1084312.

PMID: 36891388 PMC: 9986282. DOI: 10.3389/fmicb.2023.1084312.

EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks.

Shi L, Li X, Hu W, Chen H, Chen J, Fan Z Front Med (Lausanne). 2023; 10:1114673.

PMID: 36760405 PMC: 9902656. DOI: 10.3389/fmed.2023.1114673.

References

Chien T, Kao J, Liu H, Lin P, Hong J, Hsieh H . Urine sediment examination: a comparison of automated urinalysis systems and manual microscopy. Clin Chim Acta. 2007; 384(1-2):28-34. DOI: 10.1016/j.cca.2007.05.012. View

Pettipher G, Rodrigues U . Semi-automated counting of bacteria and somatic cells in milk using epifluorescence microscopy and television image analysis. J Appl Bacteriol. 1982; 53(3):323-9. DOI: 10.1111/j.1365-2672.1982.tb01278.x. View

Korber D, Lawrence J, Sutton B, Caldwell D . Effect of laminar flow velocity on the kinetics of surface recolonization by Mot(+) and Mot (-) Pseudomonas fluorescens. Microb Ecol. 2013; 18(1):1-19. DOI: 10.1007/BF02011692. View

Mukhopadhyay S, Chanda B . Multiscale morphological segmentation of gray-scale images. IEEE Trans Image Process. 2008; 12(5):533-49. DOI: 10.1109/TIP.2003.810757. View

Gracias K, McKillip J . A review of conventional detection and enumeration methods for pathogenic bacteria in food. Can J Microbiol. 2005; 50(11):883-90. DOI: 10.1139/w04-080. View

Zhao P, Li C, Rahaman M, Xu H, Ma P, Yang H . EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification, and Detection Method Evaluation. Front Microbiol. 2022; 13:829027. PMC: 9083104. DOI: 10.3389/fmicb.2022.829027. View

Rajapaksha P, Elbourne A, Gangadoo S, Brown R, Cozzolino D, Chapman J . A review of methods for the detection of pathogenic microorganisms. Analyst. 2018; 144(2):396-411. DOI: 10.1039/c8an01488d. View

Perez A, Gonzalez R . An iterative thresholding algorithm for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2011; 9(6):742-51. DOI: 10.1109/tpami.1987.4767981. View

Borics G, Lerf V, T-Krasznai E, Stankovic I, Picko L, Beres V . Biovolume and surface area calculations for microalgae, using realistic 3D models. Sci Total Environ. 2021; 773:145538. DOI: 10.1016/j.scitotenv.2021.145538. View

10.

Bloem J, Veninga M, Shepherd J . Fully automatic determination of soil bacterium numbers, cell volumes, and frequencies of dividing cells by confocal laser scanning microscopy and image analysis. Appl Environ Microbiol. 1995; 61(3):926-36. PMC: 1388375. DOI: 10.1128/aem.61.3.926-936.1995. View

11.

Kuehn M, Hausner M, Bungartz H, Wagner M, Wilderer P, Wuertz S . Automated confocal laser scanning microscopy and semiautomated image processing for analysis of biofilms. Appl Environ Microbiol. 1998; 64(11):4115-27. PMC: 106617. DOI: 10.1128/AEM.64.11.4115-4127.1998. View

12.

Huang X, Shirahama K, Li F, Grzegorzek M . Sleep stage classification for child patients using DeConvolutional Neural Network. Artif Intell Med. 2020; 110:101981. DOI: 10.1016/j.artmed.2020.101981. View

13.

Chavez de Paz L . Image analysis software based on color segmentation for characterization of viability and physiological activity of biofilms. Appl Environ Microbiol. 2009; 75(6):1734-9. PMC: 2655470. DOI: 10.1128/AEM.02000-08. View

14.

Awadh A, Kelly A, Forster-Wilkins G, Wertheim D, Giddens R, Gould S . Visualisation and biovolume quantification in the characterisation of biofilm formation in Mycoplasma fermentans. Sci Rep. 2021; 11(1):11259. PMC: 8160185. DOI: 10.1038/s41598-021-90455-5. View

15.

Puyen Z, Villagrasa E, Maldonado J, Esteve I, Sole A . Viability and biomass of Micrococcus luteus DE2008 at different salinity concentrations determined by specific fluorochromes and CLSM-image analysis. Curr Microbiol. 2011; 64(1):75-80. DOI: 10.1007/s00284-011-0033-z. View

16.

Li F, Shirahama K, Nisar M, Koping L, Grzegorzek M . Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. Sensors (Basel). 2018; 18(2). PMC: 5855052. DOI: 10.3390/s18020679. View

17.

Rani P, Kotwal S, Manhas J, Sharma V, Sharma S . Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments. Arch Comput Methods Eng. 2021; 29(3):1801-1837. PMC: 8405717. DOI: 10.1007/s11831-021-09639-x. View

18.

Couri S, Merces E, Neves B, Senna L . Digital image processing as a tool to monitor biomass growth in Aspergillus niger 3T5B8 solid-state fermentation: preliminary results. J Microsc. 2007; 224(Pt 3):290-7. DOI: 10.1111/j.1365-2818.2006.01699.x. View

19.

Chen X, Zhou X, Wong S . Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans Biomed Eng. 2006; 53(4):762-6. DOI: 10.1109/TBME.2006.870201. View

20.

Liu W, Li C, Rahaman M, Jiang T, Sun H, Wu X . Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers. Comput Biol Med. 2021; 141:105026. DOI: 10.1016/j.compbiomed.2021.105026. View