title: PlanktoVision--an automated analysis system for the identification of phytoplankton.
abstract: 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 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.
authors.affiliation: Biotechnology and Functional Genomics, Technical University of Applied Sciences, Bahnhofstraße, Wildau 15745, Germany. katja.schulze@stud-mail.uni-wuerzburg.de
authors.forename: Katja
authors.initials: K
authors.lastname: Schulze
authors.forename: Ulrich M
authors.initials: UM
authors.lastname: Tillich
authors.forename: Thomas
authors.initials: T
authors.lastname: Dandekar
authors.forename: Marcus
authors.initials: M
authors.lastname: Frohme
mesh_terms: D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods; D016571:Neural Networks, Computer; D010839:Phytoplankton / Q000166:cytology*; D012984:Software
references.citation: Anal Chem. 2012 Jan 17;84(2):839-50
references.pmid: 21755976
references.citation: Biomech Model Mechanobiol. 2012 Mar;11(3-4):461-73
references.pmid: 21744269
references.citation: Nat Methods. 2012 Jul;9(7):671-5
references.pmid: 22930834
references.citation: Sci China Life Sci. 2011 Jan;54(1):75-81
references.pmid: 21104154
references.citation: Curr Protoc Mol Biol. 2010 Oct;Chapter 14:Unit14.20
references.pmid: 20890901
references.citation: Microsc Res Tech. 2006 Sep;69(9):708-20
references.pmid: 16892193