Computer Vision-Guided Virtual Craniofacial Surgery
A Graph-Theoretic and Statistical Perspective
Samenvatting
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.
Specificaties
Inhoudsopgave
<p>Introduction</p>
<p>Graph-Theoretic Foundations</p>
<p>A Statistical Primer</p>
<p><strong>Part II: Virtual Craniofacial Reconstruction</strong></p>
<p>Virtual Single-fracture Mandibular Reconstruction</p>
<p>Virtual Multiple-fracture Mandibular Reconstruction</p>
<p><strong>Part III: Computer-aided Fracture Detection</strong></p>
<p>Fracture Detection using Bayesian Inference</p>
<p>Fracture Detection in an MRF-based Hierarchical Bayesian Framework</p>
<p>Fracture Detection using Max-Flow Min-Cut</p>
<p><strong>Part IV: Concluding Remarks</strong></p>
<p>GUI Design and Research Synopsis</p>