Computer Vision-Guided Virtual Craniofacial Surgery

A Graph-Theoretic and Statistical Perspective

By Ananda S. Chowdhury , Suchendra M. Bhandarkar

Computer Vision-Guided Virtual Craniofacial Surgery Cover Image

This unique text/reference discusses in depth the two integral components of reconstructive surgery; fracture detection, and reconstruction from broken bone fragments. It incorporates useful algorithms and relevant concepts from graph theory and statistics.

Full Description

  • ISBN13: 978-0-8572-9295-7
  • 200 Pages
  • User Level: Science
  • Publication Date: March 19, 2011
  • Available eBook Formats: PDF
  • eBook Price: $99.00
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Full Description
This unique text/reference discusses in depth the two integral components of reconstructive surgery; fracture detection, and reconstruction from broken bone fragments. In addition to supporting its application-oriented viewpoint with detailed coverage of theoretical issues, the work incorporates useful algorithms and relevant concepts from both graph theory and statistics. Topics and features: presents practical solutions for virtual craniofacial reconstruction and computer-aided fracture detection; discusses issues of image registration, object reconstruction, combinatorial pattern matching, and detection of salient points and regions in an image; investigates the concepts of maximum-weight graph matching, maximum-cardinality minimum-weight matching for a bipartite graph, determination of minimum cut in a flow network, and construction of automorphs of a cycle graph; examines the techniques of Markov random fields, hierarchical Bayesian restoration, Gibbs sampling, and Bayesian inference.
Table of Contents

Table of Contents

  1. Part I: Overview and Foundations.
  2. Introduction.
  3. Graph
  4. Theoretic Foundations.
  5. A Statistical Primer.
  6. Part II: Virtual Craniofacial Reconstruction.
  7. Virtual Single
  8. fracture Mandibular Reconstruction.
  9. Virtual Multiple
  10. fracture Mandibular Reconstruction.
  11. Part III Computer
  12. aided Fracture Detection.
  13. Fracture Detection using Bayesian Inference.
  14. Fracture Detection in an MRF
  15. based Hierarchical Bayesian Framework.
  16. Fracture Detection using Max
  17. Flow Min
  18. Cut.
  19. Part IV: Concluding Remarks.
  20. GUI Design and Research Synopsis.
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