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

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...

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Bibliographic Details
Published in:PloS one 2021-05, Vol.16 (5), p.e0250631-e0250631
Main Authors: Li, Zihan, Li, Chen, Yao, Yudong, Zhang, Jinghua, Rahaman, Md Mamunur, Xu, Hao, Kulwa, Frank, Lu, Bolin, Zhu, Xuemin, Jiang, Tao
Format: Article
Language:English
Subjects:
Biomedical engineering
Brackish water
Classification
Computer and Information Sciences
Computer programs
Datasets
Deep learning
Drafting software
Ecosystem structure
Editing
Electronic mail
Engineering
Evaluation
Funding
Human resources
Image analysis
Image processing
Image retrieval
Information engineering
Information technology
Lakes
Medical imaging
Medical research
Medical schools
Medicine
Methods
Microorganisms
Noise
Physical Sciences
Reproduction (biology)
Research and Analysis Methods
Reviews
Rotifera
Science and technology
Software
Structure-function relationships
Support vector machines
Visualization
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Summary: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:https://github.com/NEUZihan/EMDS-5.git.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0250631