A HISTOGRAM-BASED ALGORITHM FOR SEMIAUTOMATED 3-DIMENSIONAL CRANIOFACIAL MODELING

Rationale and Objectives. I conducted a study designed to facilitate t hresholding and reduce volume averaging in three-dimensional (3D) comp uted tomography (CT) craniofacial modeling. Methods. Three-dimensional CT reconstructions of orbits from two cadavers and seven clinical cas es were generated from paired axial and coronal data. A histogram-base d algorithm that was based on preliminary phantom and craniofacial spe cimen trials was applied to orbital data to identify volume averaged r egions of thin bone to be used in conjunction with standard bone thres holds. Region-of-interest measurement of the orbital floors on the ori ginal two-dimensional slice data assessed algorithm performance. Resul ts. In five of the nine cases (55%), configuration of the superimposed histograms predicted regions of volume averaging. In only one case di d such a region localize to the orbital floor. In the remaining four c ases, the air-mucosa interface deep to the orbital floor was identifie d by the histogram method. Conclusion. Operator-dependent editing rema ins superior to this histogram-based algorithm in reducing volume aver aging in 3D craniofacial modeling.