A segmentation technique to determine fat content in NMR images of beef meat
There is a constant need for new methods of meat-quality evaluation. Recent advances in the area of computer and video processing have created new ways to monitor quality in the food industry. In this paper, we describe an image-processing technique to determine fat content in beef meat. To achieve this, nuclear magnetic resonance (NMR) images of beef meat have been used. The inherent advantages of NMR images are many. Chief among these are unprecedented contrasts between the various structures present in meat, such as muscle, fat, and connective tissue. Moreover, the three-dimensional nature of the NMR method allows the analysis of isolated cross-sectional slices of the meat and the measure of the volumetric content of fat, and it is not limited to measurements of the superficially visible fat. We propose a segmentation algorithm for the detection of fat and a filtering technique to remove intensity inhomogeneities in NMR images, caused by nonuniformities of magnetic field during acquisition. Measurements have been successfully correlated with chemical analysis and digital photography. We also propose a method to quantify the distribution of fat. Our results show that the NMR technique is a promising noninvasive method to determine fat content in meat.