Systemically diseased chicken identification using multispectral images and region of interest analysis

A simple image differentiation method for the identification of systemically diseased chickens was developed and cross-system validated using two different multispectral imaging systems. The first system acquired images at three wavelengths, 460 nm, 540 nm, and 700 nm, for a batch of 164 wholesome and 176 systemically diseased chicken carcasses. The second system acquired images at four wavelengths, 488 mn, 540 run, 580 nm, and 610 nm, for a second batch of 332 wholesome and 318 systemically diseased chicken carcasses. Image masking was performed using the wavelengths of 700 nm and 610 nrn for the first and second imaging systems, respectively. The relative reflectance intensity at individual wavelengths, ratio of intensities between pairs of wavelengths, and intensity combinations based on principal component analysis (PCA) were analyzed. It was found that the wavelengths of 540 nm and 580 nm are vital for successful chicken image differentiation. With proper wavelength selection, PCA can be useful for multispectral image analysis. The wavelength of 540 nm, selected as the key wavelength, was used in both imaging systems for image differentiation. An image processing algorithm was developed to define and locate the region of interest (ROI) as the differentiation area on the image. Based on ROI analysis, a single threshold was generated for image differentiation. The average relative reflectance intensity of the ROI was calculated for each chicken image. The classification and regression trees (CART) decision tree algorithm was used to determine the threshold value to differentiate systemically diseased chickens from wholesome ones. The first differentiation threshold, based on the first image batch and generated by the decision tree method, was applied to the second image batch for cross-system validation, and vice versa. The accuracy from validation was 95.7% for wholesome and 97.7% of systemically diseased chickens for the first image batch, and 99.7% for wholesome and 93.5% for systemically diseased chickens for the second image batch. The threshold values, each generated using only one of the two image batches, were similar. The results showed that using a single key wavelength and a threshold, this simple image processing and differentiation method could be used in automated on-line applications for chicken inspection. Published by Elsevier B.V.