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.