Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches.
BACKGROUND
Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods.


RESULTS
Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%.


CONCLUSIONS
The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.
Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia.
Elham
E
Yousef Kalafi
Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia.
Wooi Boon
WB
Tan
Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, UK.
Christopher
C
Town
Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia. sarinder@um.edu.my.|Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, UK. sarinder@um.edu.my.
Sarinder Kaur
SK
Dhillon
D000818:Animals; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005399:Fishes / Q000469:parasitology*; D005880:Gills / Q000469:parasitology; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010986:Platyhelminths / Q000502:physiology*; D012867:Skin / Q000469:parasitology
Automated image recognition; Digital image processing; K-nearest neighbour; Monogenean