<p>Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been recognized as an effective tool for early detection and characterization of breast lesions. This study proposes an automated computer-aided diagnosis (CAD) system to facilitate lesion detection in DCE-MRI. The system initially identifies and crops the breast tissue reducing the processed image region and, thus, resulting in lower computational burden. Then, Otsu’s multilevel thresholding method is applied to detect and segment the suspicious regions of interest (ROIs), considering the dynamic enhancement changes across two post-contrast sequential phases. After segmentation, a two-stage false positive reduction process is applied. A rule-based stage is first applied, followed by the segmentation of control ROIs in the contralateral breast. A feature vector is then extracted from all ROIs and supervised classification is implemented using two classifiers (feed-forward backpropagation neural network (FFBPN) and support vector machine (SVM)). A dataset of 52 DCE-MRI exams was used for assessing the performance of the system in terms of accuracy, sensitivity, specificity, and precision. A total of 138 enhancing lesions were identified by an experienced radiologist and corresponded to CAD-detected ROIs. The system’s overall sensitivity was 83% when the FFBPN classifier was used and 92% when the SVM was applied. Moreover, the calculated area under curve for the SVM classifier was 0.95. Both employed classifiers exhibited high performance in identifying enhancing lesions and in differentiating them from healthy parenchyma. Current results suggest that the employment of a CAD system can expedite lesion detection in DCE-MRI images and, therefore, further research over larger datasets is warranted.</p>