Zhao Peng, Li Chen, Rahaman Md Mamunur, Xu Hao, Ma Pingli, Yang Hechen, Sun Hongzan, Jiang Tao, Xu Ning, Grzegorzek Marcin
Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
Front Microbiol. 2022 Apr 25;13:829027. doi: 10.3389/fmicb.2022.829027. eCollection 2022.
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.
环境微生物在我们周围无处不在，对人类社会的生存和发展有着重要影响。然而，环境微生物（EM）数据准备的高标准和严格要求导致现有相关数据集不足，更不用说带有地面真值（GT）图像的数据集了。这个问题严重影响了相关实验的进展。因此，本研究开发了（EMDS - 6），它包含21种环境微生物。每种环境微生物包含40张原始图像和40张GT图像，总共1680张EM图像。在本研究中，为了测试EMDS - 6的有效性。我们选择了图像处理方法中的经典算法，如图像去噪、图像分割和目标检测。实验结果表明，EMDS - 6可用于评估图像去噪、图像分割、图像特征提取、图像分类和目标检测方法的性能。EMDS - 6可在https://figshare.com/articles/dataset/EMDS6/17125025/1获取。
