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EMDS-5: Environmental Microorganism image dataset Fifth Version for multiple image analysis tasks.

  • Li, Zihan1
  • Li, Chen1
  • Yao, Yudong2
  • Zhang, Jinghua1
  • Rahaman, Md Mamunur1
  • Xu, Hao1
  • Kulwa, Frank1
  • Lu, Bolin3
  • Zhu, Xuemin4
  • Jiang, Tao5
  • 1 Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, PR China. , (China)
  • 2 Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, United States of America. , (United States)
  • 3 School of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China. , (China)
  • 4 Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States of America. , (United States)
  • 5 School of Control Engineering, Chengdu University of Information Technology, Chengdu, China. , (China)
Published Article
Public Library of Science
Publication Date
Jan 01, 2021
DOI: 10.1371/journal.pone.0250631
PMID: 33979356


Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. EMDS-5 has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiveness of EMDS-5, for each function, we select the most representative algorithms and price indicators for testing and evaluation. The image preprocessing functions contain two parts: image denoising and image edge detection. Image denoising uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the aspect of edge detection, six edge detection operators are used to detect the edges of the images, and two evaluation indicators, peak-signal to noise ratio and mean structural similarity, are used for evaluation. Image segmentation includes single-object image segmentation and multi-object image segmentation. Six methods are used for single-object image segmentation, while k-means and U-net are used for multi-object segmentation. We extract nine features from the images in EMDS-5 and use the Support Vector Machine (SVM) classifier for testing. In terms of image classification, we select the VGG16 feature to test SVM, k-Nearest Neighbors, Random Forests. We test two types of retrieval approaches: texture feature retrieval and deep learning feature retrieval. We select the last layer of features of VGG16 network and ResNet50 network as feature vectors. We use mean average precision as the evaluation index for retrieval. EMDS-5 is available at the URL:

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