@article {1547,
	title = {EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification, and Detection Method Evaluation.},
	journal = {Frontiers in Microbiology},
	volume = {13},
	year = {2022},
	month = {04/2022},
	pages = {829027},
	publisher = {Frontiers Media},
	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  (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.},
	issn = {1664-302X},
	doi = {10.3389/fmicb.2022.829027},
	author = {Zhao, Peng and Li, Chen and Rahaman, Md Mamunur and Xu, Hao and Ma, Pingli and Yang, Hechen and Sun, Hongzan and Jiang, Tao and Xu, Ning and Grzegorzek, Marcin}
}
