EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation

Peng Zhao; Chen Li; Md Mamunur Rahaman; Hao Xu; Pingli Ma; Hechen Yang; Hongzan Sun; Tao Jiang; Ning Xu; Marcin Grzegozek

doi:10.3389/978-2-88976-511-9

EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation

Peng Zhao, Chen Li, Md Mamunur Rahaman, Hao Xu, Pingli Ma, Hechen Yang, Hongzan Sun, Tao Jiang, Ning Xu, Marcin Grzegozek

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related datasets, not to mention the datasets with ground truth (GT) images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and object detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods. EMDS-6 is available at the https://figshare.com/articles/dataset/EMDS6/17125025/1.

Original language	English
Title of host publication	Artificial Intelligence in Environmental Microbiology
Editors	Mohammad-Hossein Sarrafzadeh, Seyed Soheil Mansouri, Javad Zahiri, Solange I. Mussatto
Place of Publication	Switzerland
Publisher	Frontiers Research Foundation
Pages	95-106
Number of pages	12
ISBN (Electronic)	9782889765119
DOIs	https://doi.org/10.3389/978-2-88976-511-9
Publication status	Published - 5 Jun 2022
Externally published	Yes

Publication series

Name	Frontiers in Microbiology
Publisher	Frontiers
ISSN (Electronic)	1664-8714

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

This article was originally published in Frontiers in Microbiology (2022) Vol 13, Art. 851450 at doi: 10.3389/fmicb.2022.851450.

Keywords

machine learning
microbial ecology
metagenomics
environmental monitoring
microbiology
artificial intelligence
microbial omics
predictive modeling

Access to Document

10.3389/978-2-88976-511-9Licence: CC BY

Publisher version (open access)Final published version, 8.77 MBLicence: CC BY

EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification, and detection method evaluation
Zhao, P., Li, C., Rahaman, M. M., Xu, H., Ma, P., Yang, H., Sun, H., Jiang, T., Xu, N. & Grzegorzek, M., 25 Apr 2022, In: Frontiers in Microbiology. 13, p. 1-12 12 p., 829027.
Research output: Contribution to journal › Article › peer-review

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Cite this

Zhao, P., Li, C., Rahaman, M. M., Xu, H., Ma, P., Yang, H., Sun, H., Jiang, T., Xu, N., & Grzegozek, M. (2022). EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation. In M.-H. Sarrafzadeh, S. S. Mansouri, J. Zahiri, & S. I. Mussatto (Eds.), Artificial Intelligence in Environmental Microbiology (pp. 95-106). (Frontiers in Microbiology). Frontiers Research Foundation. https://doi.org/10.3389/978-2-88976-511-9

Zhao, Peng ; Li, Chen ; Rahaman, Md Mamunur et al. / EMDS-6 : environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation. Artificial Intelligence in Environmental Microbiology. editor / Mohammad-Hossein Sarrafzadeh ; Seyed Soheil Mansouri ; Javad Zahiri ; Solange I. Mussatto. Switzerland : Frontiers Research Foundation, 2022. pp. 95-106 (Frontiers in Microbiology).

@inbook{da650d3842f24b01b1c8419d4f91a92a,

title = "EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation",

abstract = "Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related datasets, not to mention the datasets with ground truth (GT) images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and object detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods. EMDS-6 is available at the https://figshare.com/articles/dataset/EMDS6/17125025/1.",

keywords = "machine learning, microbial ecology, metagenomics, environmental monitoring, microbiology, artificial intelligence, microbial omics, predictive modeling",

author = "Peng Zhao and Chen Li and Rahaman, \{Md Mamunur\} and Hao Xu and Pingli Ma and Hechen Yang and Hongzan Sun and Tao Jiang and Ning Xu and Marcin Grzegozek",

note = "Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. This article was originally published in Frontiers in Microbiology (2022) Vol 13, Art. 851450 at doi: 10.3389/fmicb.2022.851450.",

year = "2022",

month = jun,

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doi = "10.3389/978-2-88976-511-9",

language = "English",

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editor = "Mohammad-Hossein Sarrafzadeh and Mansouri, \{Seyed Soheil\} and Javad Zahiri and Mussatto, \{Solange I.\}",

booktitle = "Artificial Intelligence in Environmental Microbiology",

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Zhao, P, Li, C, Rahaman, MM, Xu, H, Ma, P, Yang, H, Sun, H, Jiang, T, Xu, N & Grzegozek, M 2022, EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation. in M-H Sarrafzadeh, SS Mansouri, J Zahiri & SI Mussatto (eds), Artificial Intelligence in Environmental Microbiology. Frontiers in Microbiology, Frontiers Research Foundation, Switzerland, pp. 95-106. https://doi.org/10.3389/978-2-88976-511-9

EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation. / Zhao, Peng; Li, Chen; Rahaman, Md Mamunur et al.
Artificial Intelligence in Environmental Microbiology. ed. / Mohammad-Hossein Sarrafzadeh; Seyed Soheil Mansouri; Javad Zahiri; Solange I. Mussatto. Switzerland: Frontiers Research Foundation, 2022. p. 95-106 (Frontiers in Microbiology).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - EMDS-6

T2 - environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation

AU - Zhao, Peng

AU - Li, Chen

AU - Rahaman, Md Mamunur

AU - Xu, Hao

AU - Ma, Pingli

AU - Yang, Hechen

AU - Sun, Hongzan

AU - Jiang, Tao

AU - Xu, Ning

AU - Grzegozek, Marcin

N1 - Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. This article was originally published in Frontiers in Microbiology (2022) Vol 13, Art. 851450 at doi: 10.3389/fmicb.2022.851450.

PY - 2022/6/5

Y1 - 2022/6/5

N2 - Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related datasets, not to mention the datasets with ground truth (GT) images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and object detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods. EMDS-6 is available at the https://figshare.com/articles/dataset/EMDS6/17125025/1.

AB - Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related datasets, not to mention the datasets with ground truth (GT) images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and object detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods. EMDS-6 is available at the https://figshare.com/articles/dataset/EMDS6/17125025/1.

KW - machine learning

KW - microbial ecology

KW - metagenomics

KW - environmental monitoring

KW - microbiology

KW - artificial intelligence

KW - microbial omics

KW - predictive modeling

U2 - 10.3389/978-2-88976-511-9

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M3 - Chapter

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BT - Artificial Intelligence in Environmental Microbiology

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PB - Frontiers Research Foundation

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ER -

Zhao P, Li C, Rahaman MM, Xu H, Ma P, Yang H et al. EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation. In Sarrafzadeh MH, Mansouri SS, Zahiri J, Mussatto SI, editors, Artificial Intelligence in Environmental Microbiology. Switzerland: Frontiers Research Foundation. 2022. p. 95-106. (Frontiers in Microbiology). doi: 10.3389/978-2-88976-511-9

EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification and detection method evaluation

Abstract

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Bibliographical note

Keywords

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Research output

EMDS-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification, and detection method evaluation

Cite this