Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A framework for facial surgery simulation
SCCG '02 Proceedings of the 18th spring conference on Computer graphics
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Dense Surface Point Distribution Models of the Human Face
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
IEEE Transactions on Information Technology in Biomedicine
MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
Computer Methods and Programs in Biomedicine
Incremental kernel ridge regression for the prediction of soft tissue deformations
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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This paper describes a technique to approximately predict the facial morphology after standardized orthognathic ostoetomies. The technique only relies on the outer facial morphology represented as a set of surface points and does not require computed tomography (CT) images as input. Surface points may either be taken from 3D surface scans or from 3D positions palpated on the face using a tracking system. The method is based on a statistical model generated from a set of pre- and postoperative 3D surface scans of patients that underwent the same standardized surgery. The model contains both the variability of preoperative facial morphologies and the corresponding postoperative deformations. After fitting the preoperative part to 3D data from a new patient the preoperative face is approximated by the model and the prediction of the postoperative morphology can be extracted at the same time. We built a model based on a set of 15 patient data sets and tested the predictive power in leave-one-out tests for a set of relevant cephalometric landmarks. The average prediction error was found to be between 0.3 and 1.2 mm at all important facial landmarks in the relevant areas of upper and lower jaw. Thus the technique provides an easy and powerful way of prediction which avoids time, cost and radiation required by other prediction techniques such as those based on CT scans.