Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease related to metabolic syndrome. This study applied an integrated analysis based on texture, backscattering, and attenuation features in ultrasound imaging with the aim of assessing the severity of NAFLD. Ultrasound radiofrequency data obtained from 394 clinical cases were analyzed to extract three texture features (autocorrelation, sum average, and sum variance), the signal-to-noise ratio (SNR), and the slope of the center-frequency downshift (CFDS slope). The texture, SNR, and CFDS slope were combined to produce a quantitative diagnostic index (QDI) that ranged from 0 to 6. We trained the QDI using training data and then applied it to test data to assess its utility. In training data, the areas (AUCs) under the receiver operating characteristic curves for NAFLD and severe NAFLD were 0.81 and 0.84, respectively. In test data, the AUCs were 0.73 and 0.81 for NAFLD and severe NAFLD, respectively. The QDI was able to distinguish severe NAFLD and a normal liver from mild NAFLD, and it was significantly correlated with metabolic factors. This study explored the potential of using the QDI to supply information on different physical characteristics of liver tissues for advancing the ability to grade NAFLD.