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EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature
Peng Zhao1, Chen Li1, Md Mamunur Rahaman1,2
1Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Frontiers in Microbiology
|May 13, 2022
Summary
A new Environmental Microorganism Dataset Sixth Version (EMDS-6) was created to address data scarcity. This dataset aids in evaluating various image processing algorithms for environmental microorganism analysis.
Area of Science:
- Microbiology
- Computer Science
- Data Science
Background:
- Environmental microorganisms (EMs) are vital for societal development.
- Existing datasets for EMs lack sufficient ground truth (GT) images, hindering experimental progress.
- High standards for EM data preparation contribute to dataset limitations.
Purpose of the Study:
- To introduce the Environmental Microorganism Dataset Sixth Version (EMDS-6).
- To provide a comprehensive dataset with GT images for evaluating image processing algorithms.
- To facilitate research in environmental microbiology and computational analysis.
Main Methods:
- Development of the EMDS-6 dataset, comprising 21 types of EMs.
- Inclusion of 40 original and 40 GT images per EM type, totaling 1680 images.
- Utilizing classic image processing algorithms for dataset validation.
Main Results:
- EMDS-6 contains 1680 images across 21 EM types with corresponding GT images.
- Validation using image denoising, segmentation, and object detection algorithms confirmed dataset utility.
- The dataset effectively evaluates performance in image denoising, segmentation, feature extraction, classification, and object detection.
Conclusions:
- EMDS-6 is a valuable resource for advancing research on environmental microorganisms.
- The dataset supports the development and benchmarking of image analysis techniques.
- Availability of EMDS-6 at https://figshare.com/articles/dataset/EMDS6/17125025/1 promotes wider scientific use.
