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Virtual Single-Fracture Mandibular Reconstruction

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Computer Vision-Guided Virtual Craniofacial Surgery

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

In this chapter, we discuss in detail the various aspects of the problem of virtual mandibular reconstruction in the presence of a single fracture. Much of this discussion is applicable not only to situations that involve exclusively a single fracture, but also to multiple fracture scenarios wherein the fractures are fixated one at a time in the operating room. Thus, the problem of mandibular reconstruction in the case of a single fracture assumes paramount importance in most craniofacial trauma cases. We discuss various surface matching techniques such as the Iterative Closest Point (ICP) algorithm, the Data Aligned Rigidity Constrained Exhaustive Search (DARCES) algorithm and improvised variants of the ICP and DARCES algorithms. We also show how incorporating the knowledge of anatomical symmetry and biomechanical stability of a human mandible in the reconstruction process improves the overall reconstruction accuracy. The Maximum Cardinality Minimum Weight Bipartite Graph Matching algorithm, relevant concepts from Graph Automorphism, Fuzzy set-theoretic modeling, and extraction of the mean and Gaussian curvature values from the fracture surfaces are employed at various stages of the reconstruction process and are shown to improve the reconstruction accuracy.

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References

  1. Patel VV, Vannier MW, Marsh JL, Lo LJ (1996) Assessing craniofacial surgical simulation. IEEE Comput Graph Appl 16(1):46–54

    Article  Google Scholar 

  2. Enciso R, Memon A, Neumann U, Mah J (2003) The virtual cranio-facial patient project: 3D modelling and animation. In: Westwood JD, Hoffman HM, Mogel GT, Phillips R, Robb RA, Stredney D (eds) Proc eleventh medicine meets virtual reality conf, pp 65–72

    Google Scholar 

  3. Mollemans W, Schutyser F, Nadjmi N, Suetens P (2005) Very fast soft tissue predictions with mass tensor model for maxillofacial surgery planning systems. In: Proc ninth annual conf intl soc for comput aided surg, pp 491–496

    Google Scholar 

  4. Christofides N (1975) Graph theory: an algorithmic approach. Academic Press, San Diego

    MATH  Google Scholar 

  5. Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms. MIT Press, Cambridge

    MATH  Google Scholar 

  6. Keeve E, Girod S, Girod B (1996) Craniofacial surgery simulation. In: Proc fourth int conf on visualization in biomedical computing, Hamburg, Germany, pp 541–546

    Chapter  Google Scholar 

  7. Sarti A, Gori R, Marchetti C, Bianchi A, Lamberti C (2000) Maxillofacial virtual surgery from 3D CT images. In: Akay M, Marsh A (eds) VR in medicine. Springer, Berlin

    Google Scholar 

  8. Zachow S, Hedge HC, Deuflhard P (2006) Computer assisted planning in cranio-maxillofacial surgery. J Comput Inf Technol Sp Issue Comput-Based Craniofacial Model Reconstr 14(1):53–64

    Google Scholar 

  9. Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256

    Article  Google Scholar 

  10. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36

    Article  Google Scholar 

  11. Granger S, Pennec X, Roche A (2001) Rigid point-surface registration using an EM variant of ICP for computer guided oral implantology. In: Proc int conf on medical image computing and computer assisted intervention (MICCAI), Utrecht, The Netherlands, pp 752–761

    Google Scholar 

  12. Chen CS (1999) RANSAC-based DARCES: A new approach to fast automatic registration of partially overlapping range images. IEEE Trans Pattern Anal Mach Intell 21(11):1229–1234

    Article  Google Scholar 

  13. Rogers M, Graham J (2002) Robust active shape model search for medical image analysis. In: Proc int conf on medical image understanding and analysis, Portsmouth, UK, pp 1–4

    Google Scholar 

  14. Ourselin S, Roche A, Prima S, Ayache N (2000) Block matching: A general framework to improve robustness of rigid registration of medical images. In: Proc third int conf on medical robotics, imaging and computer assisted surgery, Pittsburgh, USA, pp 557–566

    Google Scholar 

  15. Bhandarkar SM, Chowdhury AS, Tang Y, Yu JC, Tollner EW (2004) Surface matching algorithms for computer aided reconstructive plastic surgery. In: Proc second IEEE int symp on biomedical imaging (ISBI), Arlington, VA, USA, pp 740–743

    Google Scholar 

  16. Bhandarkar SM, Chowdhury AS, Tang Y, Yu JC, Tollner EW (2007) Computer vision guided virtual craniofacial reconstruction. Comput Med Imaging Graph 31(6):418–427

    Article  Google Scholar 

  17. Bhandarkar SM, Chowdhury AS, Tollner EW, Yu JC, Ritter EW, Konar A (2005) Surface reconstruction for computer vision-based craniofacial surgery. In: Proc seventh IEEE int workshop on applications of computer vision (WACV), Breckenridge, CO, USA, pp 257–262

    Chapter  Google Scholar 

  18. Chowdhury AS, Bhandarkar SM, Robinson RW, Yu JC (2007) Novel graph theoretic enhancements to ICP-based virtual craniofacial reconstruction. In: Proc fourth IEEE int symp on biomedical imaging (ISBI), Arlington, VA, USA, pp 1136–1139

    Chapter  Google Scholar 

  19. Chowdhury AS, Bhandarkar SM, Robinson RW, Yu JC (2009) Virtual craniofacial reconstruction using computer vision, graph theory and geometric constraints. Pattern Recogn Lett 30(10):931–938

    Article  Google Scholar 

  20. Wang Y, Peterson B, Staib L (2000) Shape-based 3D surface correspondence using geodesics and local geometry. In: Proc first IEEE int conf on computer vision and pattern recognition (CVPR), Hilton Head Island, USA, pp 644–651

    Google Scholar 

  21. Pohl KM, Warfield SK, Kikinis R, Grimson WEL, Wells WM (2004) Coupling statistical segmentation and PCA shape modeling. In: Proc seventh int conf on medical image computing and computer assisted intervention (MICCAI), Saint Malot, France, pp 151–159

    Google Scholar 

  22. Chowdhury AS, Bhandarkar SM, Tollner EW, Zhang G, Yu JC, Ritter E (2005) A novel multifaceted virtual craniofacial surgery scheme using computer vision. In: Liu Y, Jiang T, Zhang C (eds) Proc computer vision for biomedical image applications: current techniques and future trends (CVBIA), an ICCV workshop, Beijing, China. LNCS, vol 3765, pp 146–159

    Google Scholar 

  23. Hounsfield GN (1980) Nobel award address: computed medical imaging. Med Phys 7(4):283–290

    Article  Google Scholar 

  24. Sahoo PK, Soltani S, Wong KC, Chen YC (1988) A survey of thresholding techniques. Comput Vis Graph Image Process 41:233–260

    Article  Google Scholar 

  25. Hamilton WR (1847) On quaternions. Proc R Ir Acad, Sci 3:1–16

    Google Scholar 

  26. Rangarajan A, Chui H, Mjolsness E, Pappu S, Davachi L, Goldman-Rakic PS, Duncan JS (1997) A robust point matching algorithm for autoradiograph alignment. Med Image Anal 1(4):379–398

    Article  Google Scholar 

  27. Kim W, Kak AC (1991) 3-D object recognition using bipartite matching embedded in discrete relaxation. IEEE Trans Pattern Anal Mach Intell 13(3):224–251

    Article  Google Scholar 

  28. Kuhn HW (1955) The Hungarian method for the assignment problem. Nav Res Logist Q 2:83–97

    Article  Google Scholar 

  29. Yarger R, Quek F (2000) Surface parameterization in volumetric images for feature classification. In: Proc first IEEE int symp bioinformatics and biomed engr, Arlington, VA, USA, pp 297–304

    Chapter  Google Scholar 

  30. Besl PJ (1986) Surfaces in early range image understanding. PhD dissertation, Dept of Computer Science, Michigan Univ, Ann Arbor, MI, USA

    Google Scholar 

  31. Haralick RM (1984) Digital step edges from zero crossing of second directional derivatives. IEEE Trans Pattern Anal Mach Intell 6(1):58–68

    Article  Google Scholar 

  32. Faux ID, Pratt MJ (1979) Computational geometry for design and manufacture. Wiley, New York

    MATH  Google Scholar 

  33. Suk M, Bhandarkar SM (1992) Three-dimensional object recognition from range images. Springer, Berlin

    Google Scholar 

  34. Miyajima K, Ralescu A (1994) Spatial organization in 2D segmented images: Representation and recognition of primitive spatial relations. Fuzzy Sets Syst 65:225–236

    Article  Google Scholar 

  35. Veltkamp RC, Latecki LJ (2006) Properties and performance of shape similarity measures. In: Proc tenth IFCS int conf on data science and classification, Ljubljana, Slovenia, pp 1–9

    Google Scholar 

  36. Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863

    Article  Google Scholar 

  37. Zabrodsky H, Peleg S, Avnir D (1995) Symmetry as a continuous feature. IEEE Trans Pattern Anal Mach Intell 17(12):1154–1166

    Article  Google Scholar 

  38. Sun C, Sherrah J (1997) 3D symmetry detection using the extended Gaussian image. IEEE Trans Pattern Anal Mach Intell 19(2):164–169

    Article  Google Scholar 

  39. Tuzikov AV, Sheynin SA (2002) Symmetry measure computation for convex polyhedra. J Math Imaging Vis 16:41–56

    Article  MATH  MathSciNet  Google Scholar 

  40. Tuzikov A, Colliot O, Bloch I (2002) Brain symmetry plane computation in MR images using inertia axes and optimization. In: Proc sixteenth int conf on pattern recognition (ICPR), Quebec, Canada, pp 10516–10519

    Google Scholar 

  41. Goldstein H (1982) Classical mechanics. Addison-Wesley, Reading

    Google Scholar 

  42. Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge

    Google Scholar 

  43. Prima S, Ourselin S, Ayache N (2002) Computation of the mid-sagittal plane in 3D brain images. IEEE Trans Med Imaging 21(2):122–138

    Article  Google Scholar 

  44. Ardekani B, Kershaw J, Braun M, Kanno I (1997) Automatic detection of the mid-sagittal plane in 3-D brain images. IEEE Trans Med Imaging 16(6):947–952

    Article  Google Scholar 

  45. Gefan S, Fan Y, Bertrand L, Nissanov J (2004) Symmetry-based 3D brain reconstruction. In: Proc second IEEE int symp on biomedical imaging (ISBI), Arlington, VA, pp 744–747

    Google Scholar 

  46. Junck L, Moen JG, Hutchins GD, Brown MB, Kuhl DE (1990) Correlation methods for the centering, rotation and alignment of functional brain images. J Nucl Med 31(7):1220–1226

    Google Scholar 

  47. Shames IH (1964) Mechanics of deformable solids. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  48. Wan FYM (1995) Introduction to the calculus of variations and its applications. Chapman & Hall, London

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics & Telecommunication Engineering, Jadavpur University, 188 Raja S.C. Malik Road, 700032, Kolkata, West Bengal, India

    Dr. Ananda S. Chowdhury

  2. Department of Computer Science, The University of Georgia, Boyd Graduate Studies Research Center, Room 415, 30602-7404, Athens, GA, USA

    Prof. Suchendra M. Bhandarkar

Authors
  1. Dr. Ananda S. Chowdhury
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  2. Prof. Suchendra M. Bhandarkar
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Correspondence to Ananda S. Chowdhury .

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© 2011 Springer-Verlag London Limited

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Chowdhury, A.S., Bhandarkar, S.M. (2011). Virtual Single-Fracture Mandibular Reconstruction. In: Computer Vision-Guided Virtual Craniofacial Surgery. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-296-4_4

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  • DOI: https://doi.org/10.1007/978-0-85729-296-4_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-295-7

  • Online ISBN: 978-0-85729-296-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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