Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated in recent years a great potential in screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. The aim of this work is to propose an automated system for suspicious lesion detection in DCE-MRI to support radiologists during patient image analysis. The proposed method is based on a Support Vector Machine trained with dynamic features, extracted, after a suitable pre-processing of the image, from an area pre-selected by using a pixel-based approach. The performance were evaluated by using a leave-one-patient-out approach and compared to manual segmentation made up by an experienced radiologist. Our results were also compared to other automatic segmentation methodologies: the proposed method maximises the area of correctly detected lesions while minimizing the number of false alarms (with an accuracy of 98.70%).