Automatic image analysis of plankton: future perspectives

In the future, if marine science is to achieve any progress in addressing biological diver- sity of ocean plankton, then it needs to sponsor development of new technology. One requirement is the development of high-resolution sensors for imaging field-collected and in situ specimens in a non-invasive manner. The rapid automatic categorisation of species must be accompanied by the creation of very large distributed databases in the form of high-resolution 3D rotatable images of species, which could become the standard reference source for automatic identification. These 3D images will serve as classification standards for field applications, and (in adjusted optical quality) as training templates for image analysis systems based on statistical and other pattern-matching processes. This paper sets out the basic argument for such developments and proposes a long-term solution to achieve these aims.

[1]  Joseph Katz,et al.  Submersible holocamera for detection of particle characteristics and motions in the ocean , 1999 .

[2]  S. J. Wright,et al.  Introduction to confocal microscopy and three-dimensional reconstruction. , 1993, Methods in cell biology.

[3]  M. Ohman,et al.  CalCOFI in a Changing Ocean , 2003 .

[4]  Jonathan S. Evans,et al.  Bias in human reasoning , 1990 .

[5]  C. Davis The Video Plankton Recorder (VPR) : Design and initial results , 1992 .

[6]  M. T. Vlaardingerbroek,et al.  Magnetic Resonance Imaging, Theory and Practice (2nd ed.) , 2001 .

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

[8]  Sanjay Tiwari,et al.  Optimizing multiscale texture invariants for the identification of bivalve larvae , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[9]  Stephen M. Rock,et al.  Field experiments in the control of a Jellyfish tracking ROV , 2002, OCEANS '02 MTS/IEEE.

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

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

[12]  M. Trevorrow,et al.  Measurement of Zooplankton Distributions with a High-Resolution Digital Camera System , 2003 .

[13]  Adrianna Ianora,et al.  Use of the confocal laser scanning microscope in studies on the developmental biology of marine crustaceans , 2003, Microscopy research and technique.

[14]  R R Sokal,et al.  Classification: Purposes, Principles, Progress, Prospects , 1974, Science.

[15]  M. Ohman,et al.  Linking nitrogen dynamics to climate variability off central California: a 51 year record based on 15N/14N in CalCOFI zooplankton , 2003 .

[16]  J. Cowen,et al.  Development of a scanning confocal laser microscopic technique to examine the structure and composition of marine snow , 1997 .

[17]  Lars Stemmann,et al.  Use of the Underwater Video Profiler for the Study of Aggregate Dynamics in the North Mediterranean , 2000 .

[18]  Charles S. Yentsch,et al.  An imaging-in-flow system for automated analysis of marine microplankton , 1998 .

[19]  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 .

[20]  J. Watson,et al.  Three‐dimensional spatial coordinates of individual plankton determined using underwater hologrammetry , 2000 .

[21]  B. Matsumoto Cell biological applications of confocal microscopy , 1993 .

[22]  Joseph Katz,et al.  Measurements of plankton distribution in the ocean using submersible holography , 1999 .

[23]  Ginger A. Rebstock Climatic regime shifts and decadal‐scale variability in calanoid copepod populations off southern California , 2002 .

[24]  M. H. Bundy,et al.  Innervation of copepod antennules investigated using laser scanning confocal microscopy , 1993 .

[25]  H. Edgerton,et al.  In-situ silhouette photography of Gulf Stream zooplankton , 1981 .

[26]  P. Tiselius,et al.  An in situ video camera for plankton studies:design and preliminary observations , 1998 .

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

[28]  M. M. Lavrent'ev,et al.  Computer Modelling in Tomography and Ill-Posed Problems , 2001 .

[29]  Ylenia Carotenuto,et al.  Morphological analysis of larval stages of Temora stylifera (Copepoda, Calanoida) from the Mediterranean Sea , 1999 .

[30]  E. Houde,et al.  Evaluation Of In-Situ Silhouette Photography In Investigations Of Estuarine Zooplankton And Ichthyoplankton , 1993 .

[31]  M. Ohman,et al.  Long-term changes in pelagic tunicates of the California Current , 2003 .

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

[33]  A. Solow,et al.  Estimating the taxonomic composition of a sample when individuals are classified with error , 2001 .

[34]  I. Buttino,et al.  Rapid assessment of copepod (Calanus helgolandicus) embryo viability using fluorescent probes , 2004 .

[35]  K. Wieland,et al.  The Ichthyoplankton Recorder : A video recording system for in situ studies of small-scale plankton distribution patterns , 1995 .

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

[37]  Charles S. Yentsch,et al.  9---p An imaging-inflow system for automated analysis of marine microplankton , 2006 .

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

[39]  V. Zupo,et al.  Larval development of decapod crustaceans investigated by confocal microscopy: an application to Hippolyteinermis (Natantia) , 2001 .

[40]  Rob Williams,et al.  Speciation Of The Tintinnid Genus Cymatocylis By Morphometric Analysis Of The Loricae , 1994 .

[41]  M. Picheral,et al.  Vertical distribution of suspended aggregates determined by a new underwater video profiler , 1992 .

[42]  K. Shimizu,et al.  Fundamental Study on Near-Axis Scattered Light and Its Application to Optical Computed Tomography , 2000 .

[43]  Ginger A. Rebstock Long-term stability of species composition in calanoid copepods off southern California , 2001 .

[44]  Joseph Katz,et al.  The three-dimensional flow field generated by a feeding calanoid copepod measured using digital holography , 2003, Journal of Experimental Biology.

[45]  Jules S. Jaffe,et al.  Three-dimensional swimming behavior of individual zooplankters: observations using the acoustical imaging system FishTV , 1996 .

[46]  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).

[47]  M. T. Vlaardingerbroek,et al.  Magnetic Resonance Imaging: Theory and Practice , 1996 .