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Image analysis, neural networks, and the taxonomic impediment to biodiversity studies

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Abstract

The taxonomic impediment to biodiversity studies may be influenced radically by the application of new technology, in particular, desktop image analysers and neural networks. The former offer an opportunity to automate objective feature measurement processes, and the latter provide powerful pattern recognition and data analysis tools which are able to 'learn' patterns in multivariate data. The coupling of these technologies may provide a realistic opportunity for the automation of routine species identifications. The potential benefits and limitations of these technologies, along with the development of automated identification systems are reviewed.

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References

  • Alberch, P. (1993) Museums, collections and biodiversity inventories Trends Ecol. Evol. 8, 372–5.

    Google Scholar 

  • Batra, S.W.T. (1988) Automatic image analysis for the rapid identification of Africanized honey bees. In Africanized Honey Bees and Bee Mites (G.R. Needham, ed.) pp. 260–263. Chichester: Ellis Horwood.

    Google Scholar 

  • Bishop, C.M. (1994) Neural networks and their applications. Rev. Sci. Instrum. 65, 1803–32.

    Google Scholar 

  • Boddy, L. and Morris, C.W. (1993) Analysis of flow cytometry data — a neural network approach. Binary 5, 17–22.

    Google Scholar 

  • Boddy, L., Morris, C.W., Wilkins, M.F., Tarran, G.A. and Burkill, P.H. (1994) Neural network analysis of flow cytometric data for 40 marine phytoplankton species. Cytometry 15, 283–93.

    Google Scholar 

  • Brown, L.M. Gargantini, I., Brown, J.B., Atkinson, H.J., Govindarajan, J. and Vanlerberghe, G.C. (1989) Computer-based image analysis for the automated counting and morphological description of microalgae in culture. J. Appl. Phycol. 1, 211–25.

    Google Scholar 

  • Bungay, H. and Bungay, M.L. (1991) Identifying microorganisms with a neural network. Binary 3, 51–2.

    Google Scholar 

  • Cheng, B. and Titterington, D.M. (1994) Neural networks: a review from a statistical perspective. Stat. Sci. 9, 2–54.

    Google Scholar 

  • Chesmore, D. and Monkman, G. (1994) Automated analysis of variation in Lepidoptera Entomologist 113, 171–82.

    Google Scholar 

  • Cranston, P. and Hillman, T. (1992) Rapid assessment of biodiversity using biological diversity technicians. Aust. Biol. 5, 144–54.

    Google Scholar 

  • Crick, F. (1989) The recent excitement about neural networks. Nature 337, 129–32.

    Google Scholar 

  • Daly, H.V., Hoelmer, K., Norman, P. and Allen, T. (1982) Computer-assisted measurement and identification of honey bees (Hymenoptera: Apidae). Ann. Entomol. Soc. Am. 44, 614–28.

    Google Scholar 

  • Deck, S., Morrow, C.T., Heinemann, P.H. and Sommer III, H.J. (1991) Neural networks versus traditional classifiers for machines vision inspection. ASAE Paper No. 913502.

  • Dietrich, C.H. and Pooley, C.D. (1994) Automated identification of Leafhoppers (Homoptera: Cicadellidae: Draeculacephala Ball), Ann. Entomol. Soc. Am. 87, 412–20.

    Google Scholar 

  • Dubuisson, M.P., Jain, A.K. and Jain, M.K. (1994) Segmentation and classification of bacterial culture images. J. Microbiol. Meth. 19, 279–95.

    Google Scholar 

  • Edwards, M and Morse, D.R. (1995) The potential for computer-aided identification in biodiversity research. Trends Ecol. Evol. 10, 153–8.

    Google Scholar 

  • Estep, K.W. and MacIntyre. F. (1989) Counting, sizing, and identification of algae using image analysis. SARSIA 74, 261–8.

    Google Scholar 

  • Fink, W.L. (1990) Data aquisition for morphometric analysis in systematic biology. In Proceedings of the Michigan Morphometrics Workshop, Special Publication No.2 (F.J. Rohlf and F.L. Bookstein, eds) pp. 9–19. Michigan: University of Michigan.

    Google Scholar 

  • Gamez, R. (1991) Biodiversity conservation through facilitation of its sustainable use: Costa Rica's national biodiversity institute. Trends Ecol. Evol. 6, 377–8.

    Google Scholar 

  • Gaston, K.J. (1993) Spatial patterns in the description and richness of the Hymenoptera. In Hymenoptera and Biodiversity (J. LaSalle and I.D. Gauld, eds) pp. 277–93. Wallingford: C.A.B. International Press.

    Google Scholar 

  • Gaston, K.J. and May, R.M. (1992) Taxonomy of taxonomists. Nature 356, 281–2.

    Google Scholar 

  • Ishii, T., Adachi, R., Omori, M., Shimizu, U. and Irie, H. (1987) The identification, counting, and measurement of phytoplankton by an image-processing system. J. Cons. Int. Explor. Mer. 43, 253–60.

    Google Scholar 

  • Jones, C.L., Lonergan, G.T. and Mainwaring, D.E. (1993) A rapid method for the fractal analysis of fungal colony growth using image processing. Binary 5, 171–80.

    Google Scholar 

  • Kennedy, J.M.T. (1991) Understanding image processing. Microsc. Analysis. Supplement: Image Enhancement and Analysis 25, 5–7.

    Google Scholar 

  • Kohonen, T. (1988) Self-Organisation and Associative Memory. Berlin: Springer-Verlag.

    Google Scholar 

  • Meijer, B.C., Kootstra, G.J. and Wilkinson, M.H.F. (1989) A theoretical and practical investigation into the characterization of bacterial species by image analysis. Binary 2, 21–31.

    Google Scholar 

  • Millership, S. (1993) Use of a neural network for analysis of bacterial whole cell protein fingerprints. Binary 5, 126–31.

    Google Scholar 

  • Moallemi, C (1991) Classifying cells for cancer diagnosis using neural networks. IEEE Expert 6, 8–12.

    Google Scholar 

  • Montague, G. and Morris, J. (1994) Neural-network contributions in biotechnology. Trends Biotechnol. 12, 312–24.

    Google Scholar 

  • Morris, C.W. and Boddy, L. (1992) Intelligent computing in microbiology. Binary 4, 185–8.

    Google Scholar 

  • Mound, L.A. and Gaston, K.J. (1993) Conservation and systematics — the agony and the ecstasy. In Perspectives on Insect Conservation (K.J. Gaston, T.R. New and M.J. Samways, eds) pp. 185–95. Andover: Intercept.

    Google Scholar 

  • Paul, G.C., Kent, C.A. and Thomas, C.R. (1993) An image processing method for the fully automatic measurement of vacuoles in filamentous fungi. Binary 5, 92–9.

    Google Scholar 

  • Pinto, J.D., Velten, R.K., Platner, G.R. and Oatman, E.R. (1989) Phenotypic plasticity and taxonomic characters in Trichogramma (Hymenoptera: Trichogrammatidae). Ann. Entomol. Soc. Am. 82, 414–25.

    Google Scholar 

  • Poli, R., Cagnoni, S., Livi, G., Coppini, G. and Valli, G. (1991) A neural network expert system for diagnosing and treating hypertension. IEEE Computer 24, 64–71.

    Google Scholar 

  • Rataj, T. and Schinder, J. (1991) Identification of bacteria by a multilayer neural network. Binary 3, 159–64.

    Google Scholar 

  • Richard, M.D. and Lippmann, R.P. (1991) Neural network classifiers estimates Bayesian a posteriori probabilities. Neural Comput. 3, 461–83.

    Google Scholar 

  • Rohlf, F.J. (1990) An overview of image processing, and analysis techniques for morphometrics. In Proceedings of the Michigan Morphometrics Workshop, Special Publication No.2 (F.J. Rohlf and F.L. Bookstein, eds) pp. 37–60. Michigan: University of Michigan.

    Google Scholar 

  • Rohlf. F.J. (1993) Feature extraction in systematic biology. In Advances in Computer Methods for Systematic Biology: Artificial Intelligence, Database, Computer Vision (R. Fortuner, ed.) pp. 375–92. Baltimore: The John Hopkins University Press.

    Google Scholar 

  • Rumelhart, D.E., McClelland, J.L. and PDP research group (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1. Foundations. Cambridge: MIT Press.

    Google Scholar 

  • Russ, J.C. (1995) The Image Processing Handbook, Boca Raton: CRC Press.

    Google Scholar 

  • Simpson, R., Williams, R., Ellis, R. and Culverhouse, P.F. (1992) Biological pattern recognition by neural networks, Mar. Ecol. Prog. Ser. 79, 303–8.

    Google Scholar 

  • Townes, H. and Townes, M. (1960) Ichneumon-flies of America north of Mexico: 2. Subfamilies Ephialtinae, Xordinae, Acaenitinae. U.S. Nat. Mus. Bull. 216, 1–676.

    Google Scholar 

  • Valentin, D., Abdi, H., O'Toole, A.J. and Cottrell, G.W. (1994) Connectionist models of face processing: a survey. Pattern Recognition 27, 1209–30.

    Google Scholar 

  • van de Vooren, J.G., Polder, G. and van de Heijden, G.W.A.M. (1992) Identification of mushroom cultivars using image analysis. Trans. ASAE 35, 347–50.

    Google Scholar 

  • Wilkins, M.F., Boddy, L. and Morris, C.W. (1993) Kohonen maps and learning vector quantization neural networks for analysis of multivariate biological data. Binary 6, 64–72.

    Google Scholar 

  • Yu, D.S., Kokko, E.G., Barron, J.R., Schaalje, G.B. and Gowen, B.E. (1992) Identification of ichneumonid wasps using image analysis of wings. Syst. Ent. 17, 389–95.

    Google Scholar 

  • Zerwekh, R. (1993) Information processing with neural networks. In Advances in Computer Methods for Systematic Biology: Artificial Intelligence, Databases, Computer Vision (R. Fortuner, ed.) pp. 197–212. Baltimore: The John Hopkins University Press.

    Google Scholar 

  • Zhou, Y.H., Ling, L.B. and Rohlf, F.J. (1985) Automatic description of the venation of mosquito wings from digitized images. Syst. Zool. 34, 346–58.

    Google Scholar 

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Weeks, P.J.D., Gaston, K.J. Image analysis, neural networks, and the taxonomic impediment to biodiversity studies. Biodiversity and Conservation 6, 263–274 (1997). https://doi.org/10.1023/A:1018348204573

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