Cephalometric Landmark Tracing Using Deformable Templates

Automatic detection and identification of landmarks in cephalometry is of great significance to orthognathic surgery and clinic applications. Motivated by the increasing demands of computerized cephalometric analysis, we present a tree-shaped deformable template which detects the landmark points of a grayscale cephalometric x-ray image. After normalization, a group of randomly selected images are used to train the geometric prior, and a dynamic programming algorithm enhanced by down sampling is employed to find the optimal landmark configuration. The proposed algorithm demonstrates promising detection results as well as time efficiency on both soft and hard contours. This leads to a significant improvement over the state-of-art diagnostic tools in the area of cephalometric diagnosis.