Ultrasound Image Texture Analysis for Characterizing Intramuscular Fat Content of Live Beef Cattle

Abstract

The primary factors in determining beef quality grades are the amount and distribution of intramuscular fat percentage (IMFAT). Texture analysis was applied to ultrasound B-mode images from ribeye muscle of live beef cattle to predict its IMFAT. We used wavelet transform (WT) for multiresolutional texture analysis and second-order statistics using a gray-level co-occurrence matrix (GLCM) technique. Sets of WT-and GLCM-based texture features were calculated from ultrasonic images from 207 animals and linear regression methods were used for IMFAT prediction. WT-based features included energy ratios, central moments of wavelet-decomposed subimages and wavelet edge density. The regression model using WT features provided a root mean square error (RMSE) of 1.44 for prediction of IMFAT using validation images, while that of GLCM features provided an RMSE of 1.90. The prediction models using the WT features showed potential for objective quality evaluation in the live animals.