The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus

Ali, Rozniza; Hussain, Amir; Bron, James E.; Shinn, Andrew P.

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%.

Citation

Ali, R., Hussain, A., Bron, J. E., & Shinn, A. P. (2012, November). The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus. Presented at ICONIP 2012: 19th International Conference on Neural Information Processing, Doha, Qatar

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ICONIP 2012: 19th International Conference on Neural Information Processing
• Start Date: Nov 12, 2012
• End Date: Nov 15, 2012
• Publication Date: 2012
• Deposit Date: Sep 23, 2019
• Publisher: Springer
• Volume: 7666 LNCS
• Pages: 256-263
• Series Title: Lecture Notes in Computer Science
• Series Number: 7666
• Book Title: Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part IV
• ISBN: 9783642344770
• DOI: https://doi.org/10.1007/978-3-642-34478-7_32
• Keywords: Attachment hooks, image processing, SEM, parasite, machine learning classifier
• Public URL: http://researchrepository.napier.ac.uk/Output/1793297