Ruiz-Correa S, Campos-Silvestre Y

Language: Spanish
References: 36
Page: 15-29
PDF size: 361.93 Kb.

ABSTRACT

Single-suture craniosynostosis (SSC) is the pathologic condition of premature fusion of a calvarial suture. Premature fusion produces significant cranial deformities and is associated with an increased risk of cognitive deficits and neurobehavioral impairments. For these reasons, SSC represents an important area of research that requires effective methods for characterizing cranial morphology. In this paper we evaluate a new approach that combines the use of nonlinear kernels, co-occurrences of skull shape features, a new feature selection process and standard nonlinear dimensionality reduction techniques, as a means to classify cranial malformations due to SSC using computed tomography (CT) imaging. CT images were obtained from CT studies of 102 sagittal synostosis crania, 42 metopic synostosis crania, 12 unicoronal synostosis crania and 65 nonsynostotic skulls. We validate our approach with an extensive series of experiments and show that our proposed approach outperforms the classification performance of previously published techniques, achieving classification rates above 95%.

REFERENCES

1. Cohen MM, MacLean MC. Craniosynostosis: Diagnosis, Evaluation, and Management, 2a Ed. Oxford University Press (Inglaterra), 2000.

2. Shuper A, Merlob P, Grunembaum M, Reisner SH. The incidence of isolated craniosynostosis in the newborn infant. Am J Dis Child 1985; 139(1): 85-86.

3. Speltz ML, Kapp-Simon KA, Cunningham ML, Marsh J, Dawson G. Single-suture craniosynostosis: a review of neurobehavioral research and theory. Journal of Pediatric Psychology 2004; 29(8): 651-668.

4. Lajeunie E, Le Merrer M, Marchac C, Renier D. Genetic study of scaphocephaly”. Am. J Med Gene 1996; 62: 282-285.

5. Wikie AOM. Craniosynostosis:genes and mechanisms”. Human Molecular Genetics 1997; 6(10): 1647-1656.

6. Ruiz-Correa S, Sze RW, Starr JR, Lin HJ, Speltz ML, Cunningham ML et al. New scaphocephaly severity indices of sagittal craniosynostosis. A quantitative study with cranial index quantifications. The American Cleft Palate-Craniofacial Association Journal, 2006; 43(2): 211-221.

7. Ruiz-Correa S, Starr JR , Lin HJ, Kapp-Simon K, Sze RW, Ellenbogen RG et al. New severity indices for quantifying single suture metopic craniosynostosis. Neurosurgery 2008; 63(2): 318-24; discussion 324-5.

8. Bookstein FL. Morphometric Tools for Landmark Data: Geometry and Biology. Cambridge University Press, 1991.

9. Lele SR, Richtsmeier TJ. An invariant approach to the statistical analysis of shapes, New York: Chapnan and Hall/CRC (EUA); 2001.

10. Ruiz-Correa S, Marroquin JL, Gatica-Perez D, Lin HJ, Shapiro LG, Sze RW. Discriminating cranial malformations from CT imaging by co-occurrences of skull shape features. Sometido a revisión.

11. Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts, IEEE Transactions on PAMI, 2002; (24)24: 509-522.

12. Athitsos V, Wang J, Scarloff S, Betke M. Detecting instances of classes that exhibit variable structure’. Proc. European Conference on Computer Vision}, 2006.

13. Sebastian TB, Klein PN, Kimia BB. Recognition of shapes by editing shock graphs’. Proc. International Conference of Computer Vision (ICCV), 2001: 755-762.

14. Zhu SC, Yuille AL. FORMS: a flexible object recognition and modeling system’. International Journal of Computer Vision 1996; 20: 187-212.

15. Cootes TF, Taylor CJ, Cooper DH, Graham J. Active shape models - their training and application. Computer Vision and Image Understanding 1995; 61: 38-59.

16. Captier G, Bigorre M, Rakotoarimanana JL, Leboucq N, Montoya P. Étude des variations morphologiques des scaphocéphalies. Implication pour leur systématisation. Annales de Chirurgie Plastique et Esthetique 2006; 50(6): 715-722.

17. Scholkopf B, Somola A. Learning wit kernels. The MIT Press (EUA), 2002.

18. Rangaraj RM. Biomedical image analysis. CRC Press (EUA), 2006.

19. Ruiz-Correa S, Sze RW, Lin HJ, Shapiro LG, Speltz ML and Cunningham ML. Classifying craniosynostosis deformations from skull shape imaging, Computer-Based Medical Systems (CBMS). Proceedings of the 18th IEEE Symposium 2005: 335-340.

20. Ruiz-Correa S, Gatica-Perez D, Lin HJ, Shapiro LG, and Sze RW. A Bayesian Hierarchical Model for Classifying Craniofacial Malformations from CT Imaging. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, Canada, 2008: 4063-4069.

21. Lee JA, Vereysen M. Nonlinear dimensionality reduction. Springer, New York, N.Y. (EUA), 2007.

22. Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. Journal of Machine Learning Research 2003: 993-1022.

23. Griffiths TL, Steyvers M. Finding scientific topics. Proceedings of the National Academy of Sciences 2004; 101: 5228-5235.

24. Golland P, Liang F, Mukherjee S, D. Panchenkoe D. Permutation test for classification. COLT, LNAI, 2005: 501-515.

25. Hein M, Bousquet O. Hilbertian metrics and positive definite kernels on probability measures. Proc. AISTATS 2005.

26. Gilks WR, Richardson S, Spiegelhalter DJ. Markov Chain Monte Carlo in practice. London: Chapman and Hall, 2005.

27. Richardson S, Spiegelhalter DJ. Markov Chain Monte Carlo in practice. London: Chapman and Hall (Inglaterra), 2005.

28. Dasgupta S, Hsu DJ, Verma N. A concentration theorem for projections, Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI), 2006.

29. Hofmann T. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 2001; 42: 177-196.

30. Roweis ST, Saul SK. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 2000; 290(5500): 2323-2326.

31. Tanembaum JB, de Silva V, Langford JC. A global geometric framework for nonlinear dimensionality reduction. Science 2000; 290(5500): 2319-2323.

32. Rasmussen CE, Williams CKI. Gaussian Processes for Machine Learning. MIT Press (EUA), 2006.

33. Fergus R, Perona P, Zisserman A. A visual category filter for Google images. In Proc. ECCV, Springer, 2004.

34. Cao L, Fei-Fei L. Spatially coherent latent topic model for concurrent object segmentation and classification. IEEE Intern. Conf. in Computer Vision (ICCV). 2007.

35. Huang L, Yan D, Jordan MI. DiscLDA: Discriminative learning for dimensionally reduction and classification. Advances in Neural Information Processing Systems (NIPS), 21, 2008.

36. Romano PJ. Bootstrap and randomization tests of some nonparametric hypothesis. The Annals of Statistics 1989; 17(1): 141-159.