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Classifying craniosynostosis deformations by skull shape imaging

Craniosynostosis is a serious and common disease of children, caused by premature fusion of the sutures of the skull. The resulting abnormal skull growth can lead to severe deformity, increased intra-cranial pressure, vision, hearing and breathing problems. In this work we develop an algorithmic framework to accurately classify deformations caused by sagittal craniosynostosis. The basic idea is to combine our novel cranial image shape descriptors and off-the-shelf classification technologies to encode morphological variations that characterize the synostotic skull. We demonstrate the efficacy of our approach in a series of large-scale classification experiments that compare the performance of our proposed image descriptors to those of traditional clinical indices and Fourier-based measurements.