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.

Part I: Overview and Foundations.- Introduction.- Graph-Theoretic Foundations.- A Statistical Primer.- Part II: Virtual Craniofacial Reconstruction.- Virtual Single-fracture Mandibular Reconstruction.- Virtual Multiple-fracture Mandibular Reconstruction.- Part III Computer-aided Fracture Detection.- Fracture Detection using Bayesian Inference.- Fracture Detection in an MRF-based Hierarchical Bayesian Framework.- Fracture Detection using Max-Flow Min-Cut.- Part IV: Concluding Remarks.- GUI Design and Research Synopsis.