Hybrid image processing for robust extraction of lean tissue on beef cut surfaces

Abstract

Abstract A hybrid image processing system which automatically distinguishes lean tissues in the image of a complex beef cut surface and generates the lean tissue contour has been developed. Because of the inhomogeneous distribution and fuzzy pattern of fat and lean tissues on the beef cut, conventional image segmentation and contour generation algorithms suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network to enhance the robustness of processing. The system is composed of pre-network, network, and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted to a grid pattern image, whose grid was formed as 4 × 4 pixel size. At the network stage, the normalized gray value of each grid image was taken as the network input. The pre-trained network generated the grid image output of the isolated lean tissue. A sequence of post-network processing was conducted to obtain the detailed contour of the lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human-like robust object segmentation and contour generation for the complex, fuzzy and irregular image.