Cephalometric analysis is a tool to study the craniofacial relationships, commonly used by dentists and orthodontists for skull growth analysis, diagnosis, planning, and treatment. Automatic cephalometric analysis systems rely on the precise labeling of lateral skull radiographs. This task demands from the health expert, an excellent visual acuity, and a significant amount of time in the medical image's manual labeling. This paper proposes a computational tool to support the process of semi-automatic labeling of Lateral Cephalometric Radiographs (LCR). This tool receives as input a set of images corresponding to LCR and performs automatic tasks such as filtering and digital signal processing to assist the health expert on the labeling task, using computational visual human perception models. The tool output is a set of N cephalometric landmarks (x, y) for each LCR. This tool has a set of image preprocessing techniques that clarify the manual labeling getting the accuracy required by the experts. It supports the experts in the cephalometric analysis. The second application is the creation of a new database for the development of automatic cephalometric landmark identification systems.