Changing the face of craniofacial growth curves: modelling growth and sexual dimorphism in children and adolescents using spatially dense 3D image analysis
AuthorMatthews, Harold Samuel
Document TypePhD thesis
Access StatusOpen Access
© 2018 Dr. Harold Samuel Matthews
Accurate descriptions of normal craniofacial shape are essential for accurate assessment of pathologies of the craniofacial complex. Traditionally, these descriptions, have been limited to growth curves of single measurements (e.g. head circumference) to which a patient can be compared. In this thesis we exploit advances in 3D image capture and image processing and also combine tools from statistical shape analysis and multivariate statistics to produce 3D growth curves of the entire head and face. These can serve as a normative frame of reference, against which craniofacial abnormality can be judged and we demonstrate their use for assessing individual patients. They are also a tool for comparing populations, which we demonstrate by examining emerging shape differences between boys and girls throughout childhood and adolescence, and for growth prediction and age estimation. In two studies we examine the development of sex differences in craniofacial shape (craniofacial sexual dimorphism). The first compared boys and girls within a database of one year-olds. The second compared growth curves of boys and girls derived from a cross-sectional database of 3D images of children aged from 0.05-18.6 years, to examine how and when sex differences emerge. Both studies confirmed the presence of sexual dimorphism at approximately one year-old. The second also demonstrated that sexual dimorphism emerges in primarily two phases between ages five and ten and from twelve onwards. The second of these constitute the most comprehensive study of the development of sexual dimorphism to date and both studies challenge the view that sexual dimorphism emerges as the result of sex hormones at puberty. We also use the growth curves to predict the growth of individuals from 3D photographs. We validate these predictions against a sample of 50 longitudinally collected images by comparing the predicted head at the second time point to the actual head at the second time point. We also develop them into an algorithm for estimating the age of individuals from 3D photographs.
Keywordscraniofacial growth; craniofacial sexual dimorphism; geometric morphometrics; age estimation; growth prediction; synthetic growth; classification; 3D imaging
- Click on "Export Reference in RIS Format" and choose "open with... Endnote".
- Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References