title: A Bayesian hierarchical model for classifying craniofacial malformations from CT imaging.
abstract: Single-suture craniosynostosis is a condition of the sutures of the infant's skull that causes major craniofacial deformities and is associated with an increased risk of cognitive deficits and learning/language disabilities. In this paper we adapt to classification of synostostic head shapes a Bayesian methodology that overcomes the limitations of our previously published shape representation and classification techniques. We evaluate our approach in a series of large-scale experiments and show performance superior to those of standard approaches such as Fourier descriptors, cranial spectrum, and Euclidian-distance-based analyses.
authors.affiliation: Department of Computer Science, Centro de Investigaciones en Matemáticas (CIMAT), Guanajuato, México.
authors.forename: S
authors.initials: S
authors.lastname: Ruiz-Correa
authors.forename: D
authors.initials: D
authors.lastname: Gatica-Perez
authors.forename: H J
authors.initials: HJ
authors.lastname: Lin
authors.forename: L G
authors.initials: LG
authors.lastname: Shapiro
authors.forename: R W
authors.initials: RW
authors.lastname: Sze
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology / Q000503:physiopathology*; D003398:Craniosynostoses / Q000175:diagnosis* / Q000503:physiopathology; D005583:Fourier Analysis; D006801:Humans; D007802:Language; D007859:Learning Disabilities; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D015203:Reproducibility of Results; D012886:Skull / Q000473:pathology; D014057:Tomography, X-Ray Computed / Q000379:methods*