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

摘要

Abstract: A hybrid image processing system which automatically separates lean tissues from the beef cut surface image 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 heavy computing, algorithm complexness, and even poor robustness. The proposed system utilizes an artificial neural network to enhance the robustness of processing. The system is composed of three procedures such as pre-network, network based lean tissue segmentation and post- network procedure. At the pre-network stage, gray level images of beef cuts were segmented and resized appropriate to the network inputs. Features such as fat and bone were enhanced and the enhanced input image was converted to the grid pattern image, whose grid was formed as 4 by 4 pixel size. At the network stage, the normalized gray value of each grid image was taken as the network input. Pre-trained network generated the grid image output of the isolated lean tissue. A sequence of post-network processing was followed to obtain the detailed contour of the lean tissue. The training scheme of the network and separating performance were presented and analyzed. The developed hybrid system shows the feasibility of the human like robust object segmentation and contour generation for the complex fuzzy and irregular image. !13