Cephalometric analysis requires to detect landmarks on cephalograms. Current registration techniques, such as that
use scale-invariant feature descriptor (SIFT), perform poorly on cephalograms. We proposed to improve the registration
technique for detecting the landmarks on cephalograms. The results were compared with the landmark identified by
dental professionals. Twenty digital cephalograms were collected from a dental clinic. Twenty orthodontic landmarks
were identified by dental professionals on each image; one of them was used as a template image. We automatically
locate the landmarks using a two stages approach, the global registration of the interest points between two images and a
local registration of the landmarks. In the first stage, SIFT was employed to establish point-to-point matching pairs. The
matched points on the input image were treated as a set of translation transforms from the original template image. The
consistence of the translation was controlled by applying a rectification factor defined in this study. In the second stage,
we localized the search within the suspected regions around the landmarks derived by the translations in the first stage.
Local registrations were rectified and fine-tuned until the translations close to the identified landmarks were obtained.
Our method could detect all the landmarks with error distances less than the 2mm standard set forth by previous
researcher. By improving the consistence of the translations, the performance of registration between two images was
greatly improved. This method can be used as an initial step to locate the regions around the landmarks for improving
detection in the future work.