Computer Vision-Guided Virtual Craniofacial Surgery

A Graph-Theoretic and Statistical Perspective

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Health & Well Being, Medical, Medical Science, Biochemistry, General Computing
Cover of the book Computer Vision-Guided Virtual Craniofacial Surgery by Ananda S. Chowdhury, Suchendra M. Bhandarkar, Springer London
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Author: Ananda S. Chowdhury, Suchendra M. Bhandarkar ISBN: 9780857292964
Publisher: Springer London Publication: March 19, 2011
Imprint: Springer Language: English
Author: Ananda S. Chowdhury, Suchendra M. Bhandarkar
ISBN: 9780857292964
Publisher: Springer London
Publication: March 19, 2011
Imprint: Springer
Language: English

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.

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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.

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