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Peer Review Status: Refereed
Title: The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus
Authors: Ali, Rozniza
Hussain, Amir
Bron, James
Shinn, Andrew
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Editors: Huang, T
Zeng, Z
Li, C
Leung, CS
Citation: Ali R, Hussain A, Bron J & Shinn A (2012) The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus. In: Huang T, Zeng Z, Li C, Leung CS (ed.). Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part IV. Lecture Notes in Computer Science, 7666, Berlin Heidelberg: Springer, pp. 256-263.
Keywords: Attachment hooks
image processing
machine learning classifier
Issue Date: 2012
Publisher: Springer
Series/Report no.: Lecture Notes in Computer Science, 7666
Abstract: Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their 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 KNN) 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 on 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 notifiable pathogen of Atlantic salmon, to their true class with a high degree of accuracy. The findings from the current exercise demonstrates that data subsequently imported into a K-NN classifier, outperforms several other methods of classification (i.e. LDA, MLP and SVM) that were assessed, with an average classification accuracy of 98.75%.
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Type: Part of book or chapter of book
Affiliation: University of Stirling
Computing Science - CSM Dept

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