The inspection process of a product is an important stage of a modern
production factory. This research presents a generic X-ray imaging inspection system
with application for the detection of foreign bodies in a meat product for the food
industry. The most important modules in the system are the image processing module
and the high-level detection system.
This research discusses the use of neural networks for image processing and
fuzzy-logic for the detection of potential foreign bodies found in x-ray images of
chicken breast meat after the de-boning process. The meat product is passed under a
solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a
low- and a high energy image). A series of image processing operations are applied to
the acquired image (pre-processing, noise removal, contrast enhancement). The most
important step of the image processing is the segmentation of the image into meaningful
objects. The segmentation task is a difficult one due to the lack of clarity of the acquired
X-ray images and the resulting segmented image represents not only correctly identified
foreign bodies but also areas caused by overlapping muscle regions in the meat which
appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural
network architecture was proposed for the segmentation of a X-ray dual-band image. A
number of image processing measurements were made on each object (geometrical and
grey-level based statistical features) and these features were used as the input into a
fuzzy logic based high-level detection system whose function was to differentiate
between bones and non-bone segmented regions. The results show that system's
performance is considerably improved over non-fuzzy or crisp methods. Possible noise
affecting the system is also investigated.
The proposed system proved to be robust and flexible while achieving a high
level of performance. Furthermore, it is possible to use the same approach when
analysing images from other applications areas from the automotive industry to
medicine
