Prediction of IMF Percentage of Live Cattle by Using Ultrasound Technologies with High Accuracies
The purpose of this study is to produce algorithms that are able to predict the intramuscular fat (IMF) percentage of live cattle. Two algorithms based on linear regression analysis and neural network models are developed. These two algorithms extract feature information from live cattle ultrasound images and calculate the predicted IMF percentage values. The results show that these algorithms perform better than the previous studies in the same field. A brief description of the data acquisition process, the ROI extraction, the mathematics of the feature selection methods, statistical analysis on P-value and correlation, and the outputs from Matlab programs is presented.
Image Processing Artificial Intelligence Ultrasound Image Linear Regression: Neural Network
Chengcheng Li Yufeng Zheng Agyepong Kwabena
Department of Technology Systems East Carolina University Greenville, NC, USA Department of Advanced Technology Alcorn State University Alcorn State, MS, USA
国际会议
2009 WASE International Conference on Information Engineering(2009年国际信息工程会议)(ICIE 2009)
太原
英文
1133-1137
2009-07-10（万方平台首次上网日期，不代表论文的发表时间）