It has been established that the long-term infection of chronic hepatitis C leads to the increased risk of hepatic fibrosis and hepatocellular carcinoma. Currently, histological diagnosis by invasive and painful liver biopsy is the gold standard for evaluating the hepatic fibrosis stage. Because of a side effect or patient inability to cope with the pain, it is difficult to assess the fibrosis stage frequently using liver biopsy. Recently, instead of liver biopsy, many articles have been published showing the usefulness of ultrasound elastography to evaluate the stage of hepatic fibrosis. We also reported the usefulness of real-time tissue elastography (RTE) for liver fibrosis staging in 2007. However, in our previous report, fibrosis classification was performed manually and the number of patients involved was also small. In the current study, the fibrosis staging is performed automatically using software by characterizing the elastography images. We have also increased the number of patients from 64 to 310. Thus, the aim of this study is to increase objectivity by using a newly developed automatic analysis method. We obtain the Liver Fibrosis Index (LFI), which is calculated from image features of RTE images, using multiple regression analysis performed on clinical data of 310 cases as the training data set. The correlation coefficient obtained between the LFI and the stage of hepatic fibrosis was r = 0.68, and significant differences exist between all stages of fibrosis (p < 0.001). Our new method seems promising since it has the ability to diagnose fibrosis even in the presence of inflammation.