Estimating marbling score in live cattle from ultrasound images using pattern recognition and neural network procedures.

Neural network processing of texture statistics (which parameterized longissimus muscle echograms of live cattle) resulted in marbling estimates that differed from corresponding USDA carcass marbling scores by an average of .42 marbling score units. This was more accurate (P < .001) than using the same features in a multiple regression model. Images were used from 53 cattle in the training set and from 108 cattle in the validation set. Over 500 texture statistics (including variations in direction, resolution, and step size) were screened to identify three candidates (Markovian homogeneity--step size = one; third quadrant emphasis from the bit-4, normalized run length/gray level matrix; and 12-pixel local standard deviation) for intensive analysis. The differences between the live animal estimates and carcass marbling were not much greater than the human error in assigning carcass marbling scores. When the results were subjected to receiver operating characteristic analysis, accuracies in grade classification were comparable to clinical, diagnostic imaging evaluations. It is feasible to incorporate this procedure into a computer interfaced with an ultrasound system to provide unsupervised instrument evaluation of live cattle in "near real time" (2 or 3 s).