Intelligent X-ray imaging inspection system for the food industry.

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

Intelligent X-ray imaging inspection system for the food industry.

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

Kohonen Networks for Multiscale Image Segmentation

An approach is developed to multiscale image segmentation, based on pixel classification by means of a Kohonen network. An image is described by assigning a feature pattern to each pixel, consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern representation of a training image is input to a Kohonen network, in order to obtain a description of the feature space in terms of so-called prototypical feature patterns (the weight vectors of the network). Supervised labeling of these prototypical feature patterns may be accomplished using classes derived from an a priori segmentation of the training image. We can segment any image similar to the training image by comparing the feature pattern representation of each pixel with all weight vectors, and assigning each pixel the class of the best matching weight vector. In our study we evaluated the benefit of applying features at multiple scales, as well as the effects of first- and second order..