Abstract
Craniosynostosis is a serious and common pediatric disease caused by the premature fusion of the sutures of the skull. Early fusion results in severe deformities in skull shape due to the restriction of bone growth perpendicular to the fused suture and compensatory growth in unfused skull plates. Calvarial (skull) abnormalities are frequently associated with severe impaired central nervous system functions due to brain abnormalities, increased intra-cranial pressure and abnormal build-up of cerebrospinal fluid. In this work, we develop a novel approach to efficiently classify skull deformities caused by metopic and sagittal synostoses using our newly introduced symbolic shape descriptors. We demonstrate the efficacy of our methodology in a series of large-scale classification experiments that compare the performance of our symbolic-signature-based approach to those of traditional numeric descriptors that are frequently used in clinical research. We also demonstrate an application of our symbolic descriptors in shape-based retrieval of skull morphologies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist, Soc. B 39 (1977)
Dryden, I., Mardia, K.V.: Statistical Shape Analysis. John Wiley and Sons, New York (1998)
Efron, B.: The Jackknife, the Bootstrap, and Other Resampling Plans. Society for Industrial and Applied Mathematics (1982)
Fata, J.J., Turner, M.S.: The reversal exchange technique of total calvarial reconstruction for sagittal synostosis. Plast. Reconst. Surg. 107, 1637–1646 (2001)
Guimaras-Ferreira, J., Gewalli, F., David, L., Olsson, R., Freide, H., Lauritzen, C.G.K.: Spring-mediated cranioplasty compared with the modified pi-plasty for sagittal synostosis. Scand. J. Plast. Surg. Hand Surg. 37, 209–215 (2003)
Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision. Addison-Wesley, Reading (1992)
Hofmann, T.: Unsupervised learning by Probabilistic Latent Semantic Analysis. Machine Learning 42, 177–196 (2001)
Lajeunie, E., Le Merrer, M., Marchac, C., Renier, D.: Genetic study of scaphocepaly. Am. J. Med. Gene. 62, 282–285 (1996)
Lale, S.R., Richtsmeier, J.T.: An invariant approach to statistical analysis of shape. Chapman and Hall/CRC (2001)
Lin, H.J., Ruiz-Correa, S., Shapiro, L.G., Hing, A.V., Cunningham, M.L., Speltz, M.L., Sze, R.W.: Symbolic shape descriptors for classifying craniosynostosis deformations from skull imaging. In: Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society (2005) (in press)
Lynch, M., Walsh, B.: genetic and analysis of quantitative trait. Sinauer Associates (1998)
Panchal, J., Marsh, J.L., Park, T.S., Hauffman, B., Pilgram, T., Huang, S.H.: Sagittal craniosynostosis outcome assessment for two methods of timing and interventions. Plast. Reconstr. Surg. 103, 1574–1579 (1999)
Rao, C.: Geometry of circular vectors and pattern recognition of shape of a boundary. Proc. Nat. Acad. Aci. 95, 12783 (2002)
Ruiz-Correa, S., Shapiro, L.G., Meilă, M.: A new paradigm for recognizing 3-D object shapes from range data. IEEE International Conference on Computer Vision 2, 1126–1133 (2003)
Ruiz-Correa, S., Sze, R.W., Lin, H.J., Shapiro, L.G., Speltz, M.L., Cunningham, M.L.: Classifying craniosynostosis deformations by skull shape imaging. In: Proceedings of the IEEE Conference on Computer-Based Medical Systems (2005) (in press)
Ruiz-Correa, S., Sze, R.W., Starr, J.R., Hing, A.V., Lin, H.J., Cunningham, M.L.: A Fourier-based approach for quantifying sagittal synostosis head shape. American Cleft Palate-Craniofacial Association Meeting (2005)
Ruiz-Correa, S., Sze, R.W., Starr, J.R., Lin, H.J., Speltz, M.L., Cunningham, M.L., Hing, A.V.: New scaphocephalyseverity indices of sagittal craniosynostosis: A comparative study with cranial index quantifications. Submitted to the American Cleft Palate-Craniofacial Association Journal (2005)
Scholköpf, B., Smola, A.J.: Learning with Kernels. Cambridge University Press, Cambridge (2002)
Vapnik, V.V.: Statistical Learning Theory. John Wiley and Sons, Chichester (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, H.J. et al. (2005). Efficient Symbolic Signatures for Classifying Craniosynostosis Skull Deformities. In: Liu, Y., Jiang, T., Zhang, C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569541_31
Download citation
DOI: https://doi.org/10.1007/11569541_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29411-5
Online ISBN: 978-3-540-32125-5
eBook Packages: Computer ScienceComputer Science (R0)