Textural analysis of hyperspectral images for improving contaminant detection accuracy

Previous studies demonstrated a hyperspectral imaging system has a potential for poultry fecal contaminant detection by measuring reflectance intensity. The simple image ratio at 565 and 517 nm images with optimal thresholding was able to detect fecal contaminants on broiler carcasses with high accuracy. However, differentiating false positives from real contaminants, especially cecal feces were challenging. Further image processing such as textural analysis in the spatial domain was able to reduce false positive errors. In this study, textural analysis of hyperspectral images was conducted to improve detection accuracy by reducing false positives. Specifically, textural analysis with co-occurrence matrix of hyperspectral images performed well to identify "true" contamination. In addition, co-occurrence matrix textural features including average, variance, entropy, contrast, correlation, moment of poultry hyperspectral images were investigated for selecting optimal features to represent contamination. Image pre-processing with co-occurrence textural analysis, specifically mean of co-occurrence textural feature from the matrix (0̊ angle and distance equals to one) followed by image ratio was able to improve fecal detection accuracy without additional optical filters that increase cost for system hardware of multispectral imaging system for on-line application. © Springer Science+Business Media, LLC 2008.