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

Zhao, Peng; Li, Chen; Rahaman, Md Mamunur; Xu, Hao; Ma, Pingli; Yang, Hechen; Sun, Hongzan; Jiang, Tao; Xu, Ning; Grzegorzek, Marcin

Zhao, Peng; Li, Chen; Rahaman, Md Mamunur; Xu, Hao; Ma, Pingli; Yang, Hechen; Sun, Hongzan; Jiang, Tao; Xu, Ning; Grzegorzek, Marcin.

Afiliación

Zhao P; Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Li C; Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Rahaman MM; Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Xu H; School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
Ma P; Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Yang H; Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Sun H; Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Jiang T; Department of Radiology, Shengjing Hospital, China Medical University, Shenyang, China.
Xu N; School of Control Engineering, Chengdu University of Information Technology, Chengdu, China.
Grzegorzek M; School of Arts and Design, Liaoning Petrochemical University, Fushun, China.

Front Microbiol ; 13: 829027, 2022.

Article en En | MEDLINE | ID: mdl-35547119

Palabras clave

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

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Front Microbiol Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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