PlanktoVision – an automated analysis system for the identification of phytoplankton

BackgroundPhytoplankton 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.ResultsA 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.ConclusionsThe 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.

[1]  F. N. van de Vosse,et al.  Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy , 2011, Biomechanics and Modeling in Mechanobiology.

[2]  Q. Yan,et al.  Metagenome-based analysis: A promising direction for plankton ecological studies , 2010, Science China Life Sciences.

[3]  H. Utermöhl.  Zur Vervollkommnung der quantitativen Phytoplankton-Methodik , 1958 .

[4]  Ute Mischke,et al.  Using Phytoplankton to Assess the Trophic Status of German Rivers , 2011 .

[5]  S. Jacquet,et al.  Flow cytometry sorting of freshwater phytoplankton , 2010, Journal of Applied Phycology.

[6]  Glen A. Tarran,et al.  Phytoplankton and their analysis by Flow Cytometry , 2007 .

[7]  Marc Picheral,et al.  Digital zooplankton image analysis using the ZooScan integrated system , 2010 .

[8]  Philippe Grosjean,et al.  Enumeration, measurement, and identification of net zooplankton samples using the ZOOSCAN digital imaging system , 2004 .

[9]  W. Batchelor,et al.  Shape identification and particles size distribution from basic shape parameters using ImageJ , 2008 .

[10]  Jeff Heaton,et al.  Programming Neural Networks with Encog 2 in Java , 2010 .

[11]  H. D. Buf,et al.  Automatic diatom identification , 2002 .

[12]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[13]  R. Jonker,et al.  Flow cytometry as a tool for the study of phytoplankton , 2000 .

[14]  Otto Hutzinger,et al.  Handbook of environmental chemistry , 1980 .

[15]  Christian Igel,et al.  Improving the Rprop Learning Algorithm , 2000 .

[16]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[17]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[18]  Other.  Directive 2000/60/EC of the European Parliament and of The Council of 23 October 2000 establishing a Framework for Community Action in the Field of Water Policy (Water Framework Directive) , 2000 .

[19]  Nico Stuurman,et al.  Computer Control of Microscopes Using µManager , 2010, Current protocols in molecular biology.

[20]  Horst Bunke,et al.  Symmetry based indexing of diatoms in an image database , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[21]  Matti Pietikäinen,et al.  Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features , 2009, SCIA.

[22]  Peter Gais,et al.  Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation , 2006, Microscopy research and technique.

[23]  Jeffrey S Erickson,et al.  Iowa State University From the SelectedWorks of Nicole , 2017 .

[24]  Qiao Hu,et al.  Automatic plankton image recognition with co-occurrence matrices and Support Vector Machine , 2005 .

[25]  J. Strickler,et al.  Automatic classification of field-collected dinoflagellates by artificial neural network , 1996 .

[26]  P. Utgoff,et al.  RAPID: Research on Automated Plankton Identification , 2007 .

[27]  David L. Elliott,et al.  A Better Activation Function for Artificial Neural Networks , 1993 .

[28]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  P. Culverhouse,et al.  Do experts make mistakes? A comparison of human and machine identification of dinoflagellates , 2003 .

[30]  A. Remsen,et al.  What you see is not what you catch: a comparison of concurrently collected net, Optical Plankton Counter, and Shadowed Image Particle Profiling Evaluation Recorder data from the northeast Gulf of Mexico , 2004 .

[31]  Thomas Dandekar,et al.  Ribosomal RNA phylogenetics: the third dimension , 2010, Biologia.

[32]  Charles R. Giardina,et al.  Elliptic Fourier features of a closed contour , 1982, Comput. Graph. Image Process..

[33]  Burkhard A. Hense,et al.  Use of fluorescence information for automated phytoplankton investigation by image analysis , 2008 .

[34]  Phil F. Culverhouse,et al.  Human and machine factors in algae monitoring performance , 2007, Ecol. Informatics.

[35]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[36]  BMC Bioinformatics , 2005 .

[37]  I. D. Longstaff,et al.  Improving Co-occurrence Matrix Feature Discrimination , 1995 .