Background: Lateral cephalometry is very important for the evaluation of growth, diagnosis, treatment planning and therapeutic evaluations. Considering the advantages of lateral cephalometric analyses with the automatic technique and given the existing problems, the present study was undertaken to use a new automatic technique for determination of cephalometric landmarks.
Aim: The aim of the present study was to determine the specific anatomic area by SIFT algorithm for locating cephalometric points using an automatic technique.
Methods: In this study, 110 digital lateral cephalograms were randomly selected and pre-processing was carried out on the images. Three orthodontics used these cephalograms to manually select 11 cephalometric landmarks (point A, point B, PNS, ANS, Po, Or, N, Ar, Me, Gn and Pog) on a software program which had been designed to this end. The coordinates of the selected points were saved in the database for the corresponding process of the images. After completing the software program with these data, 30 new radiographs were submitted to the software program for anatomic determination of the points. SIFT algorithm was used in the software program for the anatomic identification of the landmarks. To make a comparison, these 30 cephalograms were manually analyzed by three orthodontists in order to evaluate the accuracy of the software program at various points. Paired t-teat was used to compare the manual and computerized techniques. Statistical significance was set at P < 0.05.
Results: Based on the results, the differences between the manual and automatic methods in determining cephalometric landmarks were < 1 mm in 46% of cases, < 1.5 mm in 82% of cases and < 2 mm in 100% of cases. In addition, there were no significant difference between the two method except for points Or, Po and PNS (P > 0.05).
Conclusions: It was concluded that the designed algorithm had good performance for easy and relatively difficult points. In relation to very difficult points, too, it exhibited rather acceptable performance and located the points better than other algorithms.