Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 8, 2007
Publication Date: January 8, 2008
Citation: Yoon, S.C., Lawrence, K.C., Smith, D.P., Park, B., Windham, W.R. 2008. Bone fragment detection in chicken breast fillets using transmittance image enhancement. Transactions of the ASABE 51 (1): 331-339. Interpretive Summary: Bone fragments embedded in poultry products pose a physical hazard to consumers, especially, to children, may cause injury, and thus, need to be detected. The predominant technology for bone detection is X-ray imaging which is based on the projection of ionizing radiation through a meat sample onto an image detector. Optical imaging, which is based on non-ionizing light at the visible and near-infrared spectral range, has not been fully explored to measure the internal and functional information of uncooked or cooked poultry products. The objective of this study was to study whether optical imaging can be a viable solution for detection of bone fragments in chicken fillets. The study found a new image formation model that was applied to enhance back-illuminated images for bone detection. This finding may contribute to the development of an online and real-time bone detection system.
Technical Abstract: This paper is concerned with the detection of bone fragments embedded in de-boned skinless chicken breast fillets by modeling optical images generated by back-lighting. Imaging of chicken fillets is often dominated by multiple scattering properties of the fillets. Thus, resulting images from multiple scattering are diffused, scattered and low contrast. In this study, both transmittance and reflectance hyperspectral imaging, which is a non-ionized and non-destructive imaging modality, is investigated as an alternative method to the conventional transmittance X-ray imaging technique which is an ionizing imaging modality. As a way of reducing the influence of light scattering on images and thus increasing the image contrast, the use of a structured line light is examined along with an image formation model that separates undesirable lighting effects from an image. The image formation model, based on an illumination-transmittance model, is applied for correcting non-uniform illumination effects so that embedded bones are more easily detected by a single threshold. An automated image processing algorithm to detect bones is also proposed. Experimental results with chicken breast fillets and bone fragments are provided.