title: Symbolic shape descriptors for classifying craniosynostosis deformations from skull imaging.
abstract: Craniosynostosis is a serious condition of childhood, caused by the early fusion of the sutures of the skull. The resulting abnormal skull development can lead to severe deformities, increased intra-cranial pressure, as well as vision, hearing and breathing problems. In this work we develop a novel approach to accurately classify deformations caused by metopic and isolated sagittal synostosis. Our method combines a novel set of symbolic shape descriptors and off-the-shelf classification tools to model morphological variations that characterize the synostotic skull. We demonstrate the efficacy of our methodology in a series of large-scale classification experiments that contrast the performance of our proposed symbolic descriptors to those of traditional numeric descriptors, such as clinical severity indices, Fourier-based descriptors and cranial image quantifications.
authors.affiliation: Department of Biomedical and Health Informatics, University of Washington.
authors.forename: H
authors.initials: H
authors.lastname: Lin
authors.forename: S
authors.initials: S
authors.lastname: Ruiz-Correa
authors.forename: L G
authors.initials: LG
authors.lastname: Shapiro
authors.forename: A
authors.initials: A
authors.lastname: Hing
authors.forename: M L
authors.initials: ML
authors.lastname: Cunningham
authors.forename: M
authors.initials: M
authors.lastname: Speltz
authors.forename: R
authors.initials: R
authors.lastname: Sze