Dynamic Contrast Enhanced MRI (DCE-MRI) has today a well-established role, complementary to routine imaging techniques for breast cancer diagnosis such as mammography. Despite its undoubted clinical advantages, DCE-MRI data analysis is time-consuming and Computer Aided Diagnosis (CAD) systems are required to help radiologists. Segmentation is one of the key step of every CAD image processing pipeline, but most techniques available require human interaction. We here present the preliminary results of a fully automatic lesion detection method, capable of dealing with fat suppression image acquisition sequences, which represents a challenge for image processing algorithms due to the low SNR. The method is based on four fundamental steps: registration to correct for motion artifacts; anatomical segmentation to discard anatomical structures located outside clinically interesting lesions; lesion detection to select enhanced areas and false positive reduction based on morphological and kinetic criteria. The testing set was composed by 13 cases and included 27 lesions (10 benign and 17 malignant) of diameter > 5 mm. The system achieves a per-lesion sensitivity of 93%, while yielding an acceptable number of false positives (26 on average). The results of our segmentation algorithm were verified by visual inspection, and qualitative comparison with a manual segmentation yielded encouraging results.