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. 2021 May 12;16(5):e0250631.
doi: 10.1371/journal.pone.0250631. eCollection 2021.

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

Zihan Li  1 , Chen Li  1 , Yudong Yao  2 , Jinghua Zhang  1 , Md Mamunur Rahaman  1 , Hao Xu  1 , Frank Kulwa  1 , Bolin Lu  3 , Xuemin Zhu  4 , Tao Jiang  5
Affiliations

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

Zihan Li et al. PLoS One. .

Abstract

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. An example of EM images.
Fig 2
Fig 2. An example of 21 EM classes in EMDS-5.
Single-object GT images (SGI), Multi-object GT images (MGI).
Fig 3
Fig 3. An example of different noisy EM images using EMDS-5 images.
Fig 4
Fig 4. An example of seven edge detection results using EMDS-5 images.
Fig 5
Fig 5. An example of different single-object segmentation results using EMDS-5 images.
Fig 6
Fig 6. The structure of U-Net.
Fig 7
Fig 7. An example of different multi-object segmentation results using EMDS-5.
Fig 8
Fig 8. An example of localized EMs by GT images.
Fig 9
Fig 9. An example of image retrieval results with GLCM using EMDS-5.
Fig 10
Fig 10. A comparison of image retrieval results with four texture features using EMDS-5.
Fig 11
Fig 11. An example of image retrieval results based on VGG16 feature using EMDS-5.
Fig 12
Fig 12. A comparison of image retrieval results with two deep learning features using EMDS-5.
See this image and copyright information in PMC

References

    1. Chen Li, Kimiaki Shirahama, Marcin Grzegorzek. Environmental microbiology aided by content-based image analysis. Pattern Analysis and Applications. 2016;19(2):531–547. 10.1007/s10044-015-0498-7 - DOI
    1. Gonzalez Rafael C, Woods Richard E. Digital Image Processing (3rd Edition). Prentice-Hall. 2007;336.
    1. Pal Nikhil R, Pal Sankar K. A review on image segmentation techniques. Pattern recognition. 1993;26(9):1277–1294. 10.1016/0031-3203(93)90135-J - DOI
    1. Isabelle Guyon, Elisseeff André, Norbert Jankowski, Krzysztof Grabczewski, Dreyfus Gérard, Wlodzislaw Duch, et al.. Feature Extraction. Of Studies in Fuzziness & Soft Computing. 2006;31(7):1737–1744.
    1. Sergey Kosov, Kimiaki Shirahama, Chen Li, Marcin Grzegorzek. Environmental Microorganism Classification Using Conditional Random Fields and Deep Convolutional Neural Networks. Pattern recognition. 2017;p.S0031320317305174.

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