SEGMENTATION OF DUAL-BAND IMAGES OF X-RAY CHICKEN BREAST USING A COMPETITIVE HOPFIELD NEURAL NETWORK

摘要

The contaminant detection process of a food product is an Important stage of a modern food production factory. The large demand of bone-free for meat has lead producers to use automated systems. One such system is the automated detection of bones from raw chicken breast meat. An X-ray image of the breast meat product is taken and analysed by the system. The most important step of the process of inspecting that product is the segmentation of the image Mo meaningful objects (bones). This paper presents a Competitive Hopfield Neural Network for the segmentation of dual-band X-ray images of raw chicken breast meat, A back-propagation neural network or a simple look-up table can then be used to assign the segments into desired categories (bones, or non-bone objects).