Please use this identifier to cite or link to this item:
Appears in Collections:Computing Science and Mathematics Book Chapters and Sections
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
Author(s): Ali, Rozniza
Hussain, Amir
Bron, James
Shinn, Andrew
Contact Email:
Editor(s): 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 C (eds.) 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
Date Deposited: 8-Aug-2013
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%.
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
DOI Link: 10.1007/978-3-642-34478-7_32
Licence URL(s):

Files in This Item:
File Description SizeFormat 
Use of ASM Feature Extraction and Machine.pdfFulltext - Published Version389.49 kBAdobe PDFUnder Embargo until 3000-12-01    Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.

This item is protected by original copyright

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved

If you believe that any material held in STORRE infringes copyright, please contact providing details and we will remove the Work from public display in STORRE and investigate your claim.