Abstract #2208

Signal Intensity and Texture Feature Analysis in Contrast-Enhanced Liver MRI for Chronic Liver Disease Diagnosis

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.