Craniosynostosis, a disorder in which one or more fibrous joints of the skull fuse prematurely, causes skull deformity and is associated with increased intracranial pressure and developmental delays. Although clinicians can easily diagnose craniosynostosis and can classify its type, being able to quantify the condition is an important problem in craniofacial research. While several papers have attempted this quantification through statistical models, the methods have not been intuitive to biomedical researchers and clinicians who want to use them. The goal of this work was to develop a general platform upon which new quantification measures could be developed and tested. The features reported in this paper were developed as basic shape measures, both single-valued and vector-valued, that are extracted from a single plane projection of the 3D skull. This technique allows us to process images that would otherwise be eliminated in previous systems due to poor resolution, noise or imperfections on their CT scans. We test our new features on classification tasks and also compare their performance to previous research. In spite of its simplicity, the classification accuracy of our new features is significantly higher than previous results on head CT scan data from the same research studies.