Previous studies found that multiple view techniques improved the accuracy of lesion detection on mammograms. One of the key components in multiple view techniques was the detection of nipple location, which is the only reliable landmark on mammograms. In this study, our purpose was to develop a novel nipple detection scheme by using geometric and radiomic information extracted on digital mammography (DM). We first extracted a region of interest (ROI) to limit the region of nipple detection by using breast area and the chest wall orientation. The geometric information along the breast boundary was used to categorize the nipples into obvious and subtle types. A top hat transform was used to identify the location of obvious nipples. For subtle type, the radiomic feature matrix was calculated on straightened ROIs along the normal direction of breast boundary. A random forest classifier was trained to combine the radiomic features and to predict the location of subtle nipples. Seven hundred and twenty one DMs were collected for evaluation of our algorithm. A radiologist manually identified the location of nipples as the reference standard. It was found that the average Euclidean distances between the computer and the reference standard were 0.93±5.0 mm for obvious nipples, and 2.74±5.0 mm for subtle nipples, respectively. Future work is underway to evaluate the automated nipples on the registration of abnormalities on multiple view mammograms.