Estimating average growth trajectories in shape-space using kernel smoothing

• Published in: IEEE transactions on medical imaging Vol. 22; no. 6; pp. 747 - 753
• Main Authors: Hutton, T.J., Buxton, B.F., Hammond, P., Potts, H.W.W.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2003; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Aging - physiology; Algorithms; Biomedical imaging; Biomedical informatics; Cephalometry - methods; Child; Child, Preschool; Computation; Computed tomography; Construction; Face; Face - anatomy & histology; Face - physiology; Facies; Female; Females; Head - anatomy & histology; Head - growth & development; Human; Humans; Imaging, Three-Dimensional; Infant; Infant, Newborn; Kernel; Kernels; Male; Males; Maxillofacial Development; Middle Aged; Models, Biological; Muscles; Pattern Recognition, Automated; Sex Factors; Shape; Smoothing; Smoothing methods; Subtraction Technique; Trajectories; X-ray imaging
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2003.814784

In this paper, we show how a dense surface point distribution model of the human face can be computed and demonstrate the usefulness of the high-dimensional shape-space for expressing the shape changes associated with growth and aging. We show how average growth trajectories for the human face can be computed in the absence of longitudinal data by using kernel smoothing across a population. A training set of three-dimensional surface scans of 199 male and 201 female subjects of between 0 and 50 years of age is used to build the model.