Automated identification of copepods using digital image processing and artificial neural network. Issue 18 (December 2015)

• Record Type: Journal Article
• Authors: Leow, Lee; Chew, Li-Lee; Chong, Ving; Dhillon, Sarinder
• Is Part Of: BMC bioinformatics. Volume 16:Issue 18(2015)
• Page Start: 1
• Page End: 12
• Publication Date: 2015-12
• Subjects: automated image recognition -- copepods -- Artificial Neural Network -- digital image processing; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/1471-2105-16-S18-S4
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique. Results We developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from speciesAcartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus andTortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% forA. spinicauda, B. similis andO. aruensis, 95% forT. barbatus, 90% forO. dissimilis andP. crassirostris, 85% forO. similis andT. forcipatus ). Conclusions The methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images.