Curvelet transform is used as a multi-scale level decomposition to represent images. It was hypothesized that curvelet based texture features extraction can improve accuracy of tumor classification. The objective of this study was to differentiate the breast tumor using curvelet based features extraction followed by principal component analysis(PCA) for feature reduction and support vector machine(SVM) classifier. The study included T1 perfusion MRI data of 40 patients with breast cancer. The curvelet based texture feature using PCA with SVM classifier provided high average accuracy(0.93±0.04) in classification of malignant vs. benign and average accuracy (0.86±0.06) in characterization of high- vs. low-grade.