Main 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.

Contents:

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