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Shape-Based Classification of 3D Facial Data to Support 22q11.2DS Craniofacial Research

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Abstract

3D imaging systems are used to construct high-resolution meshes of patient’s heads that can be analyzed by computer algorithms. Our work starts with such 3D head meshes and produces both global and local descriptors of 3D shape. Since these descriptors are numeric feature vectors, they can be used in both classification and quantification of various different abnormalities. In this paper, we define these descriptors, describe our methodology for constructing them from 3D head meshes, and show through a set of classification experiments involving cases and controls for a genetic disorder called 22q11.2 deletion syndrome that they are suitable for use in craniofacial research studies. The main contributions of this work include: automatic generation of novel global and local data representations, robust automatic placement of anthropometric landmarks, generation of local descriptors for nasal and oral facial features from landmarks, use of local descriptors for predicting various local facial features, and use of global features for 22q11.2DS classification, showing their potential use as descriptors in craniofacial research.

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References

  1. Atmosukarto I, Shapiro LG, Starr JR, et al: 3D head shape quantification for infants with and without deformational plagiocephaly. Cleft Palate Craniofac J 47(4):368–377, 2009

    Article  Google Scholar 

  2. Lin H, Ruiz-Correa S, Shapiro LG, et al.: A symbolic shape-based retrieval of skull images. AMIA Annu Symp Proc pp. 1030, 2005

  3. Shprintzen RJ: Velocardiofacial syndrome. Otolaryngol Clin North Am 33 (6):1217–1240, vi, 2000

    Google Scholar 

  4. Hochheiser H, et al: The FaceBase consortium: A comprehensive program to facilitate craniofacial research. Dev Biol 355(2):175–182, 2011

    Article  PubMed  CAS  Google Scholar 

  5. Astley SJ, Clarren SK: Measuring the facial phenotype of individuals with prenatal alcohol exposure: Correlations with brain dysfunction. Alcohol Alcohol 36(2):147, 2001

    PubMed  CAS  Google Scholar 

  6. Hennessy RJ, Baldwin PA, Browne DJ, Kinsella A, Waddington JL: Three-dimensional laser surface imaging and geometric morphometrics resolve frontonasal dysmorphology in schizophrenia. Biol Psychiatry 61(10):1187–1194, 2007

    Article  PubMed  Google Scholar 

  7. Collett BR, Stott-Miller M, Kapp-Simon KA, Cunningham ML, Speltz ML: Reading in children with orofacial clefts versus controls. J Pediatr Psychol 35(2):199–208, 2010

    Article  PubMed  Google Scholar 

  8. Wang P, Barrett F, Martin E, Milonova M, et al: Automated video-based facial expression analysis of neuropsychiatric disorders. J Neurosci Methods 168(1):224–238, 2008

    Article  PubMed  Google Scholar 

  9. Romdhani S, Vetter T: Estimating 3D shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior. IEEE Comput Soc Conf Comput Vis Pattern Recogn 2:986–993, 2005

    Google Scholar 

  10. Blanz V: A learning-based high-level human computer interface for face modeling and animation. Lect Notes Comput Sci 4451:296–315, 2007. doi:10.1007/978-3-540-72348-6_15

    Article  Google Scholar 

  11. Kakadiaris IA, et al: Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach. IEEE Trans Pattern Anal Mach Intell 29(4):640–649, 2007

    Article  PubMed  Google Scholar 

  12. Dalal AB, Phadke SR: Morphometric analysis of face in dysmorphology. Comput Methods Programs Biomed 85(2):165–172, 2007. doi:10.1016/j.cmpb.2006.10.005

    Article  PubMed  Google Scholar 

  13. Colbry D, Stockman G: Canonical face depth map: A robust 3D representation for face verification. IEEE Comput Soc Conf Comput Vis Pattern Recogn, 2007. doi:10.1109/CVPR.2007.383108

  14. Ruiz-Correa S, Shapiro LG, Meila M, et al: Symbolic signatures for deformable shapes. IEEE Trans on Pattern Anal Mach Intell 28(1):75–90, 2006

    Article  Google Scholar 

  15. Akagunduz E, Ulusoy I: 3D object representation using transform and scale invariant 3D features. Int Conf Comput Vis pp. 1–8, 2007

  16. Chang KI, Bowyer KW, Flynn PJ: Multiple nose region matching for 3D face recognition under varying facial expression. IEEE Trans Pattern Anal Mach Intell 28(10):1695–1700, 2006

    Article  PubMed  Google Scholar 

  17. Whitmarsh T, Veltkamp RC, Spagnuolo M, Marini S, Haar FT: Landmark detection on 3D face scans by facial model registration. In Proceedings of the 1st International Workshop on Shape and Semantics. Citeseer, Matsushima, Japan, 2006, pp. 71–76

  18. Lee Y, Kim I, Shim J, Marshall D: 3D facial image recognition using a nose volume and curvature based eigenface. Lect Notes Comput Sci 4077:616–622, 2006

    Article  Google Scholar 

  19. Bowyer KW, Chang KI, and Flynn PJ: A survey of 3D and multi-modal 3D + 2D face recognition. Department of Computer Science and Electrical Engineering Technical Report, University of Notre Dame, 2004

  20. Romdhani S and Vetter T: 3D probabilistic feature point model for object detection and recognition. IEEE Comput Soc Conf Comput Vis Pattern Recogn, 2007, pp. 1–8

  21. Wang S, Wang Y, Jin M, Gu XD, Samaras D: Conformal geometry and its applications on 3D shape matching, recognition, and stitching. IEEE Trans Pattern Anal Mach Intell 29(7):1209–1220, 2007

    Article  PubMed  Google Scholar 

  22. Samir C, Srivastava A, Daoudi M: Three-dimensional face recognition using shapes of facial curves. IEEE Trans Pattern Anal Mach Intell 28(11):1858–1863, 2006

    Article  PubMed  Google Scholar 

  23. Boehringer S, et al: Syndrome identification based on 2D analysis software. Eur J Hum Genet 14(10):1082–1089, 2006 [Online]

    Article  PubMed  Google Scholar 

  24. Hammond P: The use of 3D face shape modelling in dysmorphology. Arch Dis Child 92(12):1120–1126, 2007. doi:10.1136/adc.2006.103507

    Article  Google Scholar 

  25. Hammond P, Hutton T, Allanson J, Campbell L, et al: 3D Analysis of Facial Morphology. Am J Med Genet 126A(4):339–348, 2004

    Article  PubMed  Google Scholar 

  26. Hammond P, et al: Discriminating power of localized three-dimensional facial morphology. Am J Hum Genet 77(6):999–1010, 2005. doi:10.1086/498396

    Article  PubMed  CAS  Google Scholar 

  27. Wilamowska K, Shapiro LG, and Heike CL: Classification of 3D face shape in 22q11.2 deletion syndrome. Int Sym Biomed Imaging, 2009, pp. 537–537

  28. Turk M, Pentland A: Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86, 1991

    Article  Google Scholar 

  29. Schroeder WJ, Martin KM, Lorensen WE: The design and implementation of an object-oriented toolkit for 3D graphics and visualization. IEEE Visual 96:93–100, 1996 [Online]

    Google Scholar 

  30. Jolliffe IT: Principal component analysis. Springer, Berlin, 2002

    Google Scholar 

  31. Shprintzen RJ: Velo-cardio-facial syndrome: 30 years of study. Dev disabil res rev 14(1):3–10, 2008. doi:10.1002/ddrr.2

    Article  PubMed  Google Scholar 

  32. Guyot L, Dubuc M, Pujol J, Dutour O, Philip N: Craniofacial anthropometric analysis in patients with 22 q 11 microdeletion. Am J Med Genet 100(1):1–8, 2001

    Article  PubMed  CAS  Google Scholar 

  33. Heike CL, Cunningham ML, Hing AV, Stuhaug E, Starr JR, et al: Picture perfect? Reliability of craniofacial anthropometry using 3D digital stereophotogrammetry in individuals with and without 22q11.2 deletion syndrome. J Plast Reconstr Surg 124(4):1261–1272, 2009

    Article  CAS  Google Scholar 

  34. Wu J, Wilamowska K, Shapiro L, Heike C: Automatic analysis of local nasal features in 22q11.2DS affected individuals. Conf Proc IEEE Eng Med Biol Soc 2009:3597–3600, 2009. doi:10.1109/IEMBS.2009.5333755

    PubMed  Google Scholar 

  35. Besl P, Jain R: Segmentation through variable-order surface fitting. IEEE Trans Pattern Anal Mach Intell 10(2):167–192, 1988

    Article  Google Scholar 

  36. Hall M: Correlation-based feature selection for machine learning. In Proceedings of the Seventeenth International Conference in Machine Learning, 1999, pp.359–366.

  37. Hall M, Frank E, Holmes G, et al: The WEKA data mining software: An update. SIGKDD Explor 11(1), 2009

  38. Domingos P, Pazzani M: On the optimality of the simple Bayesian classifier under zero-one loss. Kluwer, Boston, 1997

    Google Scholar 

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Authors and Affiliations

  1. Department of Medical Education and Biomedical Informatics, University of Washington, Box 357240, 1959 NE Pacific Street, Seattle, WA, 98195-7240, USA

    Katarzyna Wilamowska

  2. Electrical Engineering, University of Washington, Seattle, WA, USA

    Jia Wu

  3. Craniofacial Center, Seattle Children’s Hospital, Seattle, WA, USA

    Carrie Heike

  4. Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Linda Shapiro

Authors
  1. Katarzyna Wilamowska
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  2. Jia Wu
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  3. Carrie Heike
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  4. Linda Shapiro
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Correspondence to Katarzyna Wilamowska.

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Wilamowska, K., Wu, J., Heike, C. et al. Shape-Based Classification of 3D Facial Data to Support 22q11.2DS Craniofacial Research. J Digit Imaging 25, 400–408 (2012). https://doi.org/10.1007/s10278-011-9430-x

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  • DOI: https://doi.org/10.1007/s10278-011-9430-x

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