A machine vision system for automatic detection of parasites Edotea magellanica in shell-off cooked clam Mulinia edulis

In this work, a machine vision system for the automatic online detection of the parasite Edotea magellanica inside of the clam Mulinia edulis is presented. The machine vision system uses a transillumination technique to acquire images of the clam-parasite tandem. To improve the light transmission properties of the clam and parasite tissues, a novel online flattening system to flatten the clam thickness was developed. The automatic detection of the parasite in a clam is accomplished by a binary decision tree classifier that analyzes clam images. The classifier was developed using a supervised pattern-recognition approach, and proper features were created using spectral, spatial, geometrical, and biological information, such as the relative transmittance, the location of the parasite in the clam, the shape of the parasite, and the anatomical regions of the clam. The prototype system was built and tested, achieving an average classification accuracy of 98%, for a laboratory training sample set of 200 clams, and an accuracy of 73% for another set of 200 clams in an industrial setting.