In order to overcome drawbacks in manual identification of vulnerable atherosclerotic plaques, a method for automatic identification of vulnerable plaques is proposed based on computerized analysis of intravascular ultrasound images. First, the Contourlet transform is combined with the Snake model to segment images and detect lumen borders and external elastic membranes. Two categories of new features representing texture and elasticity of plaques are then automatically extracted to quantitate the features of plaques. The texture features consist of first-order statistics and featuresfrom the gray-level coocurrence matrix, and the elastic features are extracted from strain tensors estimated by nonrigid image registration. Finally, three types of features are used to design classifiers including Fisher linear discrimination, support vector machines, and generalized relevance learning vector quantization. The experimental results on 124 plaques, consisting of 36 vulnerable and 88 nonvulnerable ones, reveals that 20 morphological features, 24 texture features and 6 elastic features has significant difference (P<0.05) between the two types of plaques.The Support Vector Machine(SVM) outperformes the other two classifiers with the sensitivity, specificity, correct rate, and Youden’s index of 91.7%, 97.7%, 96.7%, and 89.4%, respectively. Therefore, the proposed method can automatically and accurately identify vulnerable plaques.