Improved lateral cephalometric superimposition using an automated image fitting technique.
OBJECTIVE
To test the feasibility of automated lateral cephalometric radiograph (LCR) superimposition using an image fitting algorithm.


METHODS
Using radiopaque markers, we identified seven cephalometric landmarks on three dry skulls, took digital LCRs on each in several rotated positions, and used a custom software program (XRay3D) to automatically superimpose each rotated image on the initial image using an anterior cranial base reference. We measured superimposition error at each landmark and adjusted image brightness levels to simulate potential fitting error due to exposure variation.


RESULTS
The greatest mean error for 24 image rotation trials of less than 10 degrees was less than 0.5 mm. Rotations of 10 degrees or more were not reliably superimposed. Errors of 0.2-1.6 mm occurred for +/-10% brightness but increased exponentially with further brightness alteration.


CONCLUSIONS
Automated superimposition of LCRs, using this fitting technique, has great potential when rotation is less than 10 degrees and brightness variation is less than 10%.
Larson
Brent E
BE
Department of Developmental and Surgical Sciences, University of Minnesota, Minneapolis, 55455, USA. larso121@umn.edu
Sievers
Matthew M
MM
Ko
Ching-Chang
CC
D000465:Algorithms; D002508:Cephalometry / Q000379:methods* / Q000706:statistics & numerical data; D003287:Contrast Media; D005240:Feasibility Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D010865:Pilot Projects; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results; D012399:Rotation; D019291:Skull Base / Q000000981:diagnostic imaging; D012984:Software
D003287:Contrast Media