Automated parasites detection in clams by transillumination imaging and pattern classification

Miguel Soto; Pablo Coelho; Jose Soto; Sergio Torres; Daniel Sbarbaro

doi:10.1117/12.909055

2 February 2012 Automated parasites detection in clams by transillumination imaging and pattern classification

Miguel Soto, Pablo Coelho, Jose Soto, Sergio Torres, Daniel Sbarbaro

Proceedings Volume 8300, Image Processing: Machine Vision Applications V; 83000F (2012) https://doi.org/10.1117/12.909055
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

Quality control of clams considers the detection of foreign objects like shell pieces, sand and even parasites. Particularly, Mulinia edulis clams are susceptible to have a parasite infection caused by the isopoda Edotea magellanica, which represents a serious commercial problem commonly addressed by manual inspection. In this work a machine vision system capable of automatically detect the parasite using a clam image is presented. The parasite visualization inside the clam is achieved by an optoelectronic imaging system based on an transillumination technique. Furthermore, automatic parasite detection in the clam's image is accomplished by a pattern recognition system designed to quantitatively describe parasite candidate zones. The extracted features are used to predict the parasite presence by means of a binary decision tree classifier. A real sample dataset of more than 155000 patterns of parasite candidate zones was generated using 190 shell-off cooked clams from the Chilean south pacific coasts. This data collection was used to train a test the classifier using cross-validation. Primary results have shown a mean parasite detection rate of 85% and a mean total correct classification of 87%, which represent a substantive improvement to the existing solutions.

Citation Download Citation

Miguel Soto, Pablo Coelho, Jose Soto, Sergio Torres, and Daniel Sbarbaro "Automated parasites detection in clams by transillumination imaging and pattern classification", Proc. SPIE 8300, Image Processing: Machine Vision Applications V, 83000F (2 February 2012); https://doi.org/10.1117/12.909055

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