Abstract
The recognition of aquatic organisms plays a crucial role in the monitoring of the pollution and for the adoption of rapid preventive actions. A compact microscopic optical imaging system is proposed in order to acquire and treat the multibands fluorescence of several pigments in phytoplankton organisms. Two algorithms for automatic recognition of phytoplankton were proposed with a minimum number of calibration parameters. The first algorithm provides a morphological recognition based on “watershed” segmentation and Fourier descriptors, while the second one builds fluorescence pigment images by “k-means” partition of intensity ratios. The operation of these algorithms was illustrated by the study of two different organisms: a cyanobacteria (Dolichospermum sp.) and an alga (Cladophora sp.). The family and the genus of these organisms were then classified into a database which is independent of the size, the orientation and the position of the specimens in the images.
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Abbreviations
- CCD:
-
charge coupled device
- Chl:
-
chlorophyll
References
Batoréu M.C.C., Dias E., Pereira P. et al.: Risk of human exposure to paralytic toxins of algal origin.–Environ. Toxicol. Phar. 19: 401–406, 2005.
Bro E., Meyer S., Genty B.: Heterogeneity of leaf CO2 assimilation during photosynthetic induction.–Plant Cell Environ. 19: 1349–1358, 1996.
Chaerle L., Leinonen I., Jones H.G. et al.: Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging.–J. Exp. Bot. 58: 773–784, 2007.
Chen M., Blankenship R.E.: Expanding the solar spectrum used by photosynthesis.–Trend. Plant Sci. 16: 427–431, 2011.
Chen X.: Skin lesion segmentation by an adaptive watershed flooding approach.–PhD Dissertation, Univ. Missouri-Rolla, 2007
Codd G.A., Lindsay J., Young J.M. et al.: Harmful Cyanobacteria.–In: Huisman J., Matthijs H.C.P., Visser P.M. (ed.): Harmful Cyanobacteria. Pp. 1–23. Springer, Dordrecht 2005.
Falco W.F., Botero E.R., Falcão E.A. et al.: In vivo observation of chlorophyll fluorescence quenching induced by gold nanoparticles.–J. Photoch. Photobio. A 225: 65–71, 2011.
Falconer I. R., Humpage A. R.: Health risk assessment of Cyanobacterial (blue-green algal) toxins in drinking water.–Ini. J. Env. Res. Pub. He. 2: 43–50, 2005.
French C.S., Young V.K.: The fluorescence spectra of red algae and the transfer of energy from phycoerythrin to phycocyanin and chlorophyll.–J. Gen. Physiol. 35: 873–890, 1952.
Gons H. J., Hakvoort H., Peters S. W. M. et al.: Optical detection of Cyanobacterial blooms.–In: Huisman J., Matthijs H.C.P., Visser P.M. (ed.): Harmful Cyanobacteria. Pp. 177–200. Springer, Dordrecht 2005.
Govindjee Shevela D.: Adventures with Cyanobacteria: a personal perspective.–Front. Plant Sci. 2: 1–17, 2011.
Guidi L., Degl’Innocenti E.: Imaging of chl a fluorescence: a tool to study abiotic stress in plants.–In: Shanker A., Venkateswarlu B. (ed.): Abiotic Stress in Plants–Mechanisms and Adaptations. Pp. 3–20. In Tech, 2011.
Hall E. L.: Computer Image Processing and Recognition. Pp. 585. Academic Press, New York 1979.
Hudnell H. K.: Cyanobacterial Harmful Algal Blooms: State of the Science and Research Needs: State of the Science and Research Needs. Pp. 2–15. Springer Sci. Business Media, New York 2008.
Komárek J., Anagnostidis K.: [Freshwater Flora of Central Europe: Chroococcales.] Pp. 548. Gustav Fischer Verlag, Stuttgart 2000 [In German]
Kühl M., Chen M., Ralph P.J. et al.: Ecology: a niche for Cyanobacteria containing chlorophyll d.–Nature 433: 820–820, 2005.
Kutser T., Vahtmäe E., Martin G.: Assessing suitability of multispectral satellites for mapping benthic macroalgal cover in turbid coastal waters by means of model simulations.–Estuar. Coast. Shelf S. 67: 521–529, 2006.
Lichtenthaler H.K., Langsdorf G., Lenk S. et al.: Chlorophyll fluorescence imaging of photosynthetic activity with the flashlamp fluorescence imaging system.–Photosynthetica 43: 355–369, 2005.
Lichtenthaler H.K., Babani F., Langsdorf G.: Chlorophyll fluorescence imaging of photosynthetic activity in sun and shade leaves of trees.–Photosynth. Res. 93: 235–244, 2007.
McConnell M.D., Koop R., Vasil’ev S. et al.: Regulation of the distribution of chlorophyll and phycobilin-absorbed excitation energy in Cyanobacteria.–A structure-based model for the light state transition.–Plant Physiol. 130: 1201–1212, 2002.
Metcalf J.S., Richer R., Cox P.A. et al.: Cyanotoxins in desert environments may present a risk to human health.–Sci. Total Environ. 421: 118–123, 2012.
Poryvkina L., Babichenko S., Leeben A.: Analysis of phytoplankton pigments by excitation spectra of fluorescence.–EARSeL eProceedings 1: 224–232 2000.
Quiblier C., Wood S., Echenique-Subiabre I. et al.: A review of current knowledge on toxic benthic freshwater Cyanobacteria–Ecology, toxin production and risk management.–Water Res. 47: 5464–5479, 2013.
Rabinowitch E., Govindjee U.: Photosynthesis. Pp. 102–123. John Wiley & Sons, New York 2013.
Richardson T.L., Lawrenz E., Pinckney J.L. et al.: Spectral fluorometric characterization of phytoplankton community composition using the Algae Online Analyser®.–Water Res. 44: 2461–2472, 2010.
See J.H., Campbell L., Richardson T.L. et al.: Combining new technologies for determination of phytoplankton community structure in the Northern Gulf of Mexico.–J. Phycol. 41: 305–310, 2005.
Seppälä J., Ylöstalo P., Kaitala S. et al.: Ship-of-opportunity based phycocyanin fluorescence monitoring of the filamentous Cyanobacteria bloom dynamics in the Baltic Sea.–Estuar. Coast. Shelf S. 73: 489–500, 2007.
Seppälä J., Babichenko S., Leeben A. et al.: Fluorescence diagnostics of phytoplankton bloom in Baltic, Institute of Ecology, Tallinn Estonia.–In: Proceedings of the Six International Conference on Remote Sensing for Marine and Coastal Environments. Pp. 377–383. Environmental Research Institute of Michigan, Ann Arbor 2000.
Shedbalkar V.P., Rebeiz C.A.: Chloroplast biogenesis: Determination of the molar extinction coefficients of divinyl chl a and b and their pheophytins.–Anal. Biochem. 207: 261–266, 1992.
Stamatakis K., Tsimilli-Michael M., Papageorgiou G.C.: On the question of the light-harvesting role of ß-carotene in photosystem II and photosystem I core complexes.–Plant Physiol. Bioch. 81: 121–127, 2014.
Tang X., Stewart W. K., Vincent L. et al.: Automatic plankton image recognition.–In: Panigrahi S., Ting K.C. (ed.): Artificial Intelligence for Biology and Agriculture. Pp. 177–199. Springer, Dordrecht 1998.
Trytek M., Janik E., Maksymiec W. et al.: The spectral and catalytic studies of chlorophylls and pheophytins in mimetic biotransformation of a-pinene.–J. Photoch. Photobio. A 223: 14–24, 2011.
van den Hoek C.: Revision of the European Species of Cladophora. Pp. 363. Brill Acad. Publ., Leiden 1963.
van Wittenberghe S., Alonso L., Verrelst J. et al.: Upward and downward solar-induced chlorophyll fluorescence yield indices of four tree species as indicators of traffic pollution in Valencia.–Environ. Pollut. 173: 29–37, 2013.
Vasconcelos V.M.: Cyanobacterial toxins in Portugal: effects on aquatic animals and risk for human health.–Braz. J. Med. Biol. Res. 32: 249–254, 1999.
Wacklin P., Hoffmann L., Komárek J.: Nomenclatural validation of the genetically revised Cyanobacterial genus Dolichospermum (Ralfs ex Bornet et Flahault) comb.–Fottea 9: 59–64, 2009.
Wang H., Moss R.H., Chen X. et al.: Watershed segmentation of dermoscopy images using a watershed technique.–Skin Res. Technol. 16: 378–384, 2010.
Wang H., Moss R.H., Chen X. et al.: Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images.–Comput. Med. Imag. Grap. 35: 116–120, 2011.
Wilhelm C., Lenarz-Weiler I.: Energy transfer and pigment composition in three Chl b-containing light-harvesting complexes isolated from Mantoniella squamata (Prasinophyceae), Chlorella fusca (Chlorophyceae) and Sinapis alba.–Photosynth. Res. 13: 101–111, 1987.
Wörmke S., Mackowski S., Brotosudarmo T. H. et al.: Monitoring fluorescence of individual chromophores in peridinin–chlorophyll–protein complex using single molecule spectroscopy.–BBA-Bioenergetics 1767: 956–964, 2007.
Yao H., Duan Q., Li D. et al.: An improved K-means clustering algorithm for fish image segmentation.–Math. Comput. Model. 58: 790–798, 2013.
Yentsch C.S., Phinney D.A.: Spectral fluorescence: an ataxonomic tool for studying the structure of phytoplankton populations.–J. Plankton Res. 7: 617–632, 1985.
Zarco-Tejada P.J., Berni J.A.J., Suárez L.: Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection.–Remote Sens. Environ. 113: 1262–1275, 2009.
Ziegmann M., Abert M., Müller M. et al.: Use of fluorescence fingerprints for the estimation of bloom formation and toxin production of Microcystis aeruginosa.–Water Res. 44: 195–204, 2010.
Acknowledgments
This work was supported by Europe (FEDER), Lorraine region and the project “BioCapTech”. The authors wish to thank emeritus Pr. Jean-Claude Pihan and Dr Cécile Dupouy (IRD, Nouméa) for their advices during the course of this study.
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Lauffer, M., Genty, F., Margueron, S. et al. Morphological recognition with the addition of multi-band fluorescence excitation of chlorophylls of phytoplankton. Photosynthetica 55, 434–442 (2017). https://doi.org/10.1007/s11099-016-0663-2
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DOI: https://doi.org/10.1007/s11099-016-0663-2