Objective To discuss the applicability of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features in diagnosis of breast lesions between benign and malignant by comparing the classification performances of morphology, texture and time intensity curves (TIC). Methods Twelve morphological features, 56 gray level co-occurrence matrix (GLCM) based texture features and 11 features of TIC are calculated for the data of 224 breast lesions (benign 82, malignant 142). To evaluate the performance of discriminating malignant from benign lesions, these features are studied based on average square distance criteria and SVM classifier. Results The TIC features (sensitivity 0.9366, specificity 0.8293, AUC 0.9495) perform best and the performance of texture features (sensitivity 0.9225, specificity 0.7195, AUC 0.8835) are better than morphological features (sensitivity 0.8451, specificity 0.6951, AUC 0.8079).The combination of the 9 features (smoothness, compactness, entropy, etc.) performs optimally (AUC 0.9642). Conclusions The TIC features are of high sensitivity for malignancy and improve the identification rate of malignant lesions in breast computer-aided diagnosis. The comprehensive analysis of the morphological features, texture features and TIC features can reduce the misdiagnosis rate and improve the specificity of malignant lesions at the same time.