Application of pattern-recognition techniques to liver magnetic resonance imaging

Object of our study was to investigate the feasibility of improving liver Magnetic Resonance Imaging (MRI) specificity by subjecting liver MRI to quantitative analyses under pattern recognition framework. All imaging were performed with a 1.5 Tesla scanner. Four quantitative features were measured from each patient sample image showing the largest dimension of the lesion. K-Nearest-Neighbor (KNN) was applied for classification. Higher than 90 percent accuracy was achieved for differentiation between abnormal from normal. Less success was achieved in differentiation among different types of abnormalities.