Literature DB >> 35547119

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification, and Detection Method Evaluation.

Peng Zhao1, Chen Li1, Md Mamunur Rahaman1,2, Hao Xu1, Pingli Ma1, Hechen Yang1, Hongzan Sun3, Tao Jiang4, Ning Xu5, Marcin Grzegorzek6.   

Abstract

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 Environmental Microorganism Dataset Sixth Version (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.
Copyright © 2022 Zhao, Li, Rahaman, Xu, Ma, Yang, Sun, Jiang, Xu and Grzegorzek.

Entities:  

Keywords:  environmental microorganism; feature extraction; image classification; image denoising; image segmentation; object detection

Year:  2022        PMID: 35547119      PMCID: PMC9083104          DOI: 10.3389/fmicb.2022.829027

Source DB:  PubMed          Journal:  Front Microbiol        ISSN: 1664-302X            Impact factor:   6.064


  7 in total

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Journal:  IEEE Trans Image Process       Date:  2007-05       Impact factor: 10.856

2.  Recurrent residual U-Net for medical image segmentation.

Authors:  Md Zahangir Alom; Chris Yakopcic; Mahmudul Hasan; Tarek M Taha; Vijayan K Asari
Journal:  J Med Imaging (Bellingham)       Date:  2019-03-27

3.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Authors:  Shaoqing Ren; Kaiming He; Ross Girshick; Jian Sun
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2016-06-06       Impact factor: 6.226

4.  A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers.

Authors:  Peng Zhao; Chen Li; Md Mamunur Rahaman; Hao Xu; Hechen Yang; Hongzan Sun; Tao Jiang; Marcin Grzegorzek
Journal:  Front Microbiol       Date:  2022-03-02       Impact factor: 5.640

5.  A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches.

Authors:  Jiawei Zhang; Chen Li; Md Mamunur Rahaman; Yudong Yao; Pingli Ma; Jinghua Zhang; Xin Zhao; Tao Jiang; Marcin Grzegorzek
Journal:  Artif Intell Rev       Date:  2021-09-29       Impact factor: 9.588

6.  Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.

Authors:  Md Mamunur Rahaman; Chen Li; Yudong Yao; Frank Kulwa; Mohammad Asadur Rahman; Qian Wang; Shouliang Qi; Fanjie Kong; Xuemin Zhu; Xin Zhao
Journal:  J Xray Sci Technol       Date:  2020       Impact factor: 1.535

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

Authors:  Zihan Li; Chen Li; Yudong Yao; Jinghua Zhang; Md Mamunur Rahaman; Hao Xu; Frank Kulwa; Bolin Lu; Xuemin Zhu; Tao Jiang
Journal:  PLoS One       Date:  2021-05-12       Impact factor: 3.240

  7 in total
  1 in total

1.  A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements.

Authors:  Jiawei Zhang; Chen Li; Md Mamunur Rahaman; Yudong Yao; Pingli Ma; Jinghua Zhang; Xin Zhao; Tao Jiang; Marcin Grzegorzek
Journal:  Arch Comput Methods Eng       Date:  2022-09-06       Impact factor: 8.171

  1 in total

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