Previous research has shown that a fuzzy C-means (FCM) approach to computerized lesion analysis has the potential to aid radiologists in the interpretation of dynamic contrast-enhanced MRI (DCE-MRI) breast exams. ^{1, 2} Our purpose in this study was to optimize the performance of the FCM approach with respect to binary (benign/malignant) breast lesion classification in DCE-MRI. We used both raw (calculated from kinetic data points) and empirically fitted³ kinetic features for this study. FCM was used to automatically select a characteristic kinetic curve (CKC) based on intensity-time point data of voxels within each lesion, using four different kinetic criteria: (1) maximum initial enhancement, (2) minimum shape index, (3) maximum washout, and (4) minimum time to peak. We extracted kinetic features from these CKCs, which were merged using linear discriminant analysis (LDA), and evaluated with receiver operating characteristic (ROC) analysis. There was comparable performance for methods 1, 2, and 4, while method 3 was inferior. Next, we modified use of the FCM method by calculating a feature vector for every voxel in each lesion and using FCM to select a characteristic feature vector (CFV) for each lesion. Using this method, we achieved performance similar to the four CKC methods. Finally, we generated lesion color maps using FCM membership matrices, which facilitated the visualization of enhancing voxels in a given lesion.