Jihun Oh1, Diego Martin2, Xiaoping P. Hu3
1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States; 2Department of Medical Imaging, University of Arizona, Tucson, AZ, United States; 3Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
This paper describes our work of using features derived from contrast-enhanced liver MR images for providing a quantitative assessment of chronic liver disease severity. We first examined the mean slope of contrast uptake in hepatobiliary phase and demonstrated that it is significantly correlated with fibrosis score. We also examined several texture measures in equilibrium phase using Gabor filtering and grey level co-occurrence matrix and built a supervised maximum a posteriori classifier using these features to predict the disease severity. The classifier was evaluated by cross-validation and shown to be highly robust in predicting fibrosis score.