Efficient symbolic signatures for classifying craniosynostosis skull deformities
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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Craniosynostosis is a serious and common desiease 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 encodemorphical 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 clinic alindices and Fourier-based measurements.