Feature Extraction and Classification for Multiple Species of Gyrodactylus Ectoparasite
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Date
2015-03-03
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TELKOMNIKA Indonesian Journal of Electrical Engineering
Abstract
Active Shape Models (ASM) are applied to the attachment hooks of several species of
Gyrodactylus, including the notifiable pathogen G. salaris, to assign each species to its true species type.
ASM is used as a feature extraction tool to select information from hook images that can be used as input
data into trained classifiers. Linear (i.e. LDA and K-NN) and non-linear (i.e. MLP and SVM) models are
used to classify Gyrodactylus species. Species of Gyrodactylus, ectoparasitic monogenetic flukes of fish,
are difficult to discriminate and identify according to morphology alone and their speciation currently
requires taxonomic expertise. The current exercise sets out to confidently classify species, which in this
example includes a species which is a notifiable pathogen of Atlantic salmon, to their true class with a high
degree of accuracy. The findings from the current exercise demonstrates that import of ASM data into a
MLP classifier, outperforms several other methods of classification (i.e. LDA, K-NN and SVM) that were
assessed, with an average classification accuracy of 98.72%.
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Keywords
marginal hooks, feature extraction, gyrodactylus, machine learning