We develop several morphological image filters that can be useful for computer-aided medical image diagnosis. Some computer-aided diagnosis (CAD) systems for lung cancer and breast cancer have been developed to assist radiologist's diagnosis work. The CAD systems for lung cancer can automatically detect pathological changes (pulmonary nodules) with a high true positive rate (TP) even under low false positive rate (FP) conditions. On the other hand, the conventional CAD systems for breast cancer can automatically detect some pathological changes (calcifications and masses), but TP for other changes such as architectural distortion is still very low. Motivated by the radiologist's cognitive process to increase TP for breast cancer, we propose new methods to extract novelmorphological features from X-ray mammography. Simulation results demonstrate the effectiveness of the morphological methods for detecting tumor shadows.