Computer aided brain tumor detection is an efficient research area in brain image processing. In this study, a methodology called GWMAC-CT (gradient based watershed marked active contours and curvelet transform) is proposed to detect the brain tumors in magnetic resonance (MR) images. The implemented system is based on skull removing, segmentation of region of interest (ROI), feature extraction, and ROI classification as tumor or nontumor. The proposed GWMAC is a two-stage segmentation method which includes gradient based watershed transform (GWT) and improved active contours. The rough ROIs obtained with GWT are utilized as initial contours for the improved active contours method instead of marking initial contours manually. Curvelet transform-based features of the exact ROI contours are classified via well-known classification methods such as support vector machine (SVM), K-nearest neighbors, random forest tree, and Naive Bayes. Experiments are carried out on a set of brain MR images from BRATS database to demonstrate the effectiveness of the proposed method. The performance evaluators such as accuracy, kappa statistics, false positive rate, precision, F1-measure, and area under ROC curve are calculated as 96.81%, 0.927, 0.046, 0.905, 0.95, and 0.977, respectively with SVM.