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PlanktoVision – an automated analysis system for the identification of phytoplankton

Please always quote using this URN: urn:nbn:de:bvb:20-opus-96395
  • Background Phytoplankton communities are often used as a marker for the determination of fresh water quality. The routine analysis, however, is very time consuming and expensive as it is carried out manually by trained personnel. The goal of this work is to develop a system for an automated analysis. Results A novel open source system for the automated recognition of phytoplankton by the use of microscopy and image analysis was developed. It integrates the segmentation of the organisms from the background, the calculation of a largeBackground Phytoplankton communities are often used as a marker for the determination of fresh water quality. The routine analysis, however, is very time consuming and expensive as it is carried out manually by trained personnel. The goal of this work is to develop a system for an automated analysis. Results A novel open source system for the automated recognition of phytoplankton by the use of microscopy and image analysis was developed. It integrates the segmentation of the organisms from the background, the calculation of a large range of features, and a neural network for the classification of imaged organisms into different groups of plankton taxa. The analysis of samples containing 10 different taxa showed an average recognition rate of 94.7% and an average error rate of 5.5%. The presented system has a flexible framework which easily allows expanding it to include additional taxa in the future. Conclusions The implemented automated microscopy and the new open source image analysis system - PlanktoVision - showed classification results that were comparable or better than existing systems and the exclusion of non-plankton particles could be greatly improved. The software package is published as free software and is available to anyone to help make the analysis of water quality more reproducible and cost effective.show moreshow less

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Metadaten
Author: Katja Schulze, Ulrich M. Tillich, Thomas Dandekar, Marcus Frohme
URN:urn:nbn:de:bvb:20-opus-96395
Document Type:Journal article
Faculties:Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Language:English
Parent Title (English):BMC Bioinformatics
Year of Completion:2013
Source:In: BMC Informatics (2013) 14: 115, doi:10.1186/1471-2105-14-115
URL:http://www.biomedcentral.com/1471-2105/14/115
DOI:https://doi.org/10.1186/1471-2105-14-115
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Tag:Bioinformatik
Release Date:2014/04/29
Collections:Open-Access-Publikationsfonds / Förderzeitraum 2013
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung