Statistical Model-Based Thresholding of Multispectral Images for Contaminant Detection on Poultry Carcasses
Developing an algorithm to decide the presence or absence of fecal contamination on the surface of poultry carcasses is critical to food safety. The global threshold strategy for a band-ratio algorithm has been known to be limited to pixel-basis detection. In an attempt to develop a statistical decision rule for carcass-basis detection from multispectral images, probability density functions of both contaminated and uncontaminated materials were estimated by parametric and non-parametric methods. We found that uncontaminated poultry carcasses could be modeled by a Gaussian distribution, whereas contaminated materials were non-Gaussian. A kernel density estimator was used to analyze the non-Gaussian characteristic of the contaminated materials on a transformed projection axis. A linear mixture of the density functions was introduced to model the observations made on the projection axis. A new detection algorithm was designed using the mixture model and tested for 496 birds (248 dirty and 248 clean birds). A test on the sample birds revealed that the algorithm needed at least 12 contaminant pixels to reach the perfect detection results. The test also showed a false-positive rate of less than 5%.