Automatic classification of field-collected dinoflagellates by artificial neural network

Abstract

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonon~ists in identifying these species was compared to that achieved by 2 art~fi-cial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Dlscnm~nant Analysis The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66 %, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56 %.

Identification of 72 phytoplankton species by radial basis function neural network analysis of flow cytometric data

“…Microscopic analysis in the laboratory is laborious and time-consuming, abundance estimates are uncertain due to limitations on the number of cells that can be counted, and interesting phenomena cannot be followed up directly because analysis is often performed a long time after sampling. The use of image analysis is one possibility, and has been used successfully to discriminate 23 dinoflagellate species (Culverhouse et al 1996). It is, however, computationally intensive.…”

Reconnaissance d'algues toxiques par vision artificielle et réseau de neurones /

“…Je désire remercier mon directeur de recherche, M. Jean-François Méthot, pour la Dans la littérature, il est fait mention du fait que certaines expériences portant sur la classification du phytoplancton au moyen de la vision artificielle étaient vouées à l'échec dû à la complexité des formes et aux espèces apparentées apparaissant dans le même échantillon [6,7,23]; les espèces apparentées, non toxiques, pouvant être une source de nombreuses fausses alertes. Divers travaux ont démontré que l'utilisation de paramètres cytométriques permettrait une très bonne discrimination sur des appareils autres que le FLOWCAM [43].…”

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

“…Finally, experiments prove the usefulness of this system, where 201 images are used for classifier training and 299 images are used for testing, and an error rate of 11% is achieved. As a further development of the above work, Culverhouse et al (1996Culverhouse et al ( , 2000 compare the classification performance of four different classifiers in a WM classification task, including multi-layer ANN, RBF network, k-NN and quadratic discriminant analysis algorithms. In this research, basic pixel based features, like pixel values converted by Fast Fourier Transform (FFT), are extracted to represent the WM images.…”

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