Extracting objects related to a fold in the cerebral cortex ("sulcus features") from human brain magnetic resonance imaging data has applications in morphometry, landmark-based registration, and anatomical labeling. In prior work, sulcus features such as surfaces, fundi and pits have been extracted separately. Here we define and extract nested sulcus features in a hierarchical manner from a cortical surface mesh having curvature or depth values. Our experimental results show that the nested features are comparable to features extracted separately using other methods, and that they are consistent across subjects and with manual label boundaries. Our open source feature extraction software will be made freely available as part of the Mindboggle project (http://www.mindboggle.info).