Assessment of ZooImage as a tool for the classification of zooplankton

ZooImage, image analysis software, was evaluated to determine its ability to differentiate between zooplankton groups in preserved zooplankton samples collected in Prince William Sound, Alaska. A training set of 53 categories were established to train the software for automatic recognition. Using the Random forest algorithm, ZooImage identified particles in the training set with less than 13% error. Despite reasonable results with the training set, however, ZooImage was less effective when this training set was used to identify particles from field-collected zooplankton samples. When all particles were examined, ZooImage had an accuracy of 81.7% but this dropped to 63.3% when discard particles (e.g. marine snow and fibers) were removed from total particles. Copepods, the numerically dominant organisms in most samples, were examined separately and were correctly identified 67.8% of the time. Further investigation suggested size was effective in determining identifications; medium size copepods (e.g. Pseudocalanus sp., Acartia sp.) were accurately identified 73.3% of the time. ZooImage can provide a coarse level of taxonomic classification and we anticipate continued improvement to this software should further enhance automatic identification of preserved zooplankton samples.

[1]  W. Hagen,et al.  Biomass and abundance , 2000 .

[2]  Mar Ecol Ser Prog,et al.  Biological pattern recognition by neural networks , .

[3]  Kenneth W. Estep,et al.  Counting, sizing, and identification of algae using image analysis , 1989 .

[4]  A. D. Poularikas,et al.  Automated sizing, counting and identification of zooplankton by pattern recognition , 1984 .

[5]  Qiao Hu,et al.  Accurate automatic quantification of taxa-specific plankton abundance using dual classification with correction , 2006 .

[6]  P. Wiebe,et al.  From the Hensen net toward four-dimensional biological oceanography , 2003 .

[7]  D. Markle,et al.  An evaluation of accuracy, precision, and speed of several zooplankton subsampling techniques , 1982 .

[8]  Phil F. Culverhouse,et al.  Automatic categorisation of five species of Cymatocylis (Protozoa, Tintinnida) by artificial neural network , 1994 .

[9]  Diffraction patterns as a tool to recognize copepods , 1996 .

[10]  Georgia Coastal,et al.  Future marine zooplankton research—a perspective , 2001 .

[11]  Einar Hjörleifsson,et al.  MacImage: a user-friendly image-analysis system for the accurate mensuration of marine organisms , 1986 .

[12]  Josué Álvarez-Borrego,et al.  IDENTIFICATION OF SPECIES OF CALANOID COPEPODS USING A NEW INVARIANT CORRELATION ALGORITHM , 2001 .

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

[14]  Manfred Rolke,et al.  Size structure analysis of zooplankton samples by means of an automated image analyzing system , 1984 .

[15]  Lawrence O. Hall,et al.  Learning to recognize plankton , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[16]  Phil F. Culverhouse,et al.  Expert visual classification and neural networks: can general solutions be found? , 1994, Proceedings of OCEANS'94.

[17]  R. T. Cooney,et al.  Ecosystem controls of juvenile pink salmon ( Onchorynchus gorbuscha ) and Pacific herring ( Clupea pallasi ) populations in Prince William Sound, Alaska , 2001 .

[18]  He Huang,et al.  Automatic Plankton Image Recognition , 1998, Artificial Intelligence Review.

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

[20]  J. Boldt,et al.  A Bioenergetics Approach to Estimating Consumption of Zooplankton by Juvenile Pink Salmon in Prince William Sound, Alaska , 2003 .

[21]  Josué Álvarez-Borrego,et al.  Discrimination between Acartia (Copepoda: Calanoida) species using their diffraction pattern in a position, rotation invariant digital correlation , 2003 .

[22]  Roger Harris,et al.  ICES zooplankton methodology manual , 2000 .

[23]  Makoto Omori,et al.  The identification, counting, and measurement of phytoplankton by an image-processing system , 1987 .

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

[25]  Peter H. Wiebe,et al.  3 Collecting zooplankton , 2000 .

[26]  Peter H. Wiebe,et al.  Video plankton recorder estimates of copepod, pteropod and larvacean distributions from a stratified region of Georges Bank with comparative measurements from a MOCNESS sampler , 1996 .

[27]  W. K. Stewart,et al.  Rapid visualization of plankton abundance and taxonomic composition using the Video Plankton Recorder , 1996 .

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

[29]  K. V. Embleton,et al.  Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method , 2003 .

[30]  R. Tibshirani,et al.  Flexible Discriminant Analysis by Optimal Scoring , 1994 .

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

[32]  Lawrence O. Hall,et al.  Active learning to recognize multiple types of plankton , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[33]  ipred : Improved Predictors , 2009 .

[34]  A. D. Cross,et al.  Distribution, size, and interannual, seasonal and diel food habits of northern Gulf of Alaska juvenile pink salmon, Oncorhynchus gorbuscha , 2005 .

[35]  A. Richardson.  In hot water: zooplankton and climate change , 2008 .

[36]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[37]  Scott Samson,et al.  A system for high-resolution zooplankton imaging , 2001 .

[38]  Phil F. Culverhouse,et al.  Automatic image analysis of plankton: future perspectives , 2006 .

[39]  Phil F. Culverhouse,et al.  Biological pattern recognition by neural networks , 1991 .

[40]  Oceans,et al.  Oceans engineering for today's technology and tomorrow's preservation : proceedings , 1994 .

[41]  Lawrence O. Hall,et al.  Recognizing plankton images from the shadow image particle profiling evaluation recorder , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[42]  Gabriel Gorsky,et al.  The Autonomous Image Analyzer - enumeration, measurement and identification of marine phytoplankton , 1989 .

[43]  J. Boldt,et al.  Seasonal and Geographic Variation in Juvenile Pink Salmon Diets in the Northern Gulf of Alaska and Prince William Sound , 2003 .

[44]  Peter H. Wiebe,et al.  Zooplankton Methodology Manual , 2000 .