Objective To explore the feasibility of using digital imaging processing (DIP)to extract EUS image parameters for the differential diagnosis of autoimmune pancreatitis (AIP)and chronic pancreati-tis (CP).Methods A total of 81 patients with AIP and 100 patients with CP diagnosed from May 2005 to January 2013 were recruited to this study.A total of 105 parameters of 9 categories were extracted from the region of interest by using computer-based techniques.Then the distance between class algorithm and se-quential forward selection (SFS)algorithm were used for a better combination of features.A support vector machine (SVM)predictive model was built,trained,and validated.Results Overall,25 parameters of 5 categories were selected as a better combination of features when the incidence of accurate category was max (90.08%).A total of 181 sample sets were randomly divided into a training set and a testing set by using two different algorithms and 200 random tests were performed.The average accuracy,sensitivity,specificity, the positive and negative predictive values of AIP based on the half-and-half method were (86.04 ± 3.15)%,(83.66 ±6.57)%,(88.54 ±4.37)%,(85.96 ±4.44)% and (87.12 ±4.39)%,respective-ly.Conclusion Computer-aided diagnosis of EUS images is objective and non-invasive,which can improve the accuracy in differentiating AIP from CP.This technology provides a new valuable diagnostic tool for the clinical determination of AIP.