Proposition Of Local Automatic Algorithm For Landmark Detection In 3D Cephalometry

Mohammed ED-DHAHRAOUY, hicham riri, manal ezzahmouly, abdelmajid elmoutaouakkil, farid bourzgui, Hakima Aghoutan


Objectives: this study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. Methods: 3D images of twenty patient obtained from CBCT (Cone Beam Computed Tomography) equipment were used for automatic identification of twelve landmarks located on maxillo-facial structures. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The method was implemented in C++ language.

Results: The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm.

Conclusions: Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.


landmarks detection, Automatic algorithm, CBCT image, 3D Cephalometry, Orthodontics.


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