Titlebook: Computer Vision-Guided Virtual Craniofacial Surgery; A Graph-Theoretic an Ananda S. Chowdhury,Suchendra M. Bhandarkar Book 2011 Springer-Ve

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Virtual Multiple-Fracture Mandibular Reconstructionruction is modeled as one of maximum-weight graph matching which, in contrast, is shown to have a polynomial-time complexity in the worst case. The surface matching algorithms described in the previous chapter are used to achieve pairwise registration of the fracture surfaces. The global shape of th

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Fracture Detection Using Bayesian Inferenced reduced risk for potential postoperative complications. In this chapter, we propose a scheme for identification of stable fracture points in well displaced or major fractures in an input CT image sequence. First, a set of candidate fracture points is generated by identifying points of high curvatu

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Fracture Detection in an MRF-Based Hierarchical Bayesian Frameworkof a hairline fracture or minor fracture, the decision regarding surgical intervention is less clear since the surgeon can rely on natural bone healing for fracture reduction without having to perform reconstructive surgery. In this chapter, we propose a Markov Random Field (MRF)-based hierarchical

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Fracture Detection Using Max-Flow Min-Cut) algorithm by Ford and Fulkerson. This approach for identification and localization of fractures is shown to yield more promising results in the case of minor fractures while requiring very little preprocessing of the input image data. We first model a sequence of 2D CT image slices as a collection

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GUI Design and Research Synopsisch of the previous chapters has concluded with a section outlining future research directions. However, in those sections, we discussed the future work from a relatively narrow perspective of the specific problem being dealt with in that chapter. In contrast, in the final section of this chapter, we

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Book 2011t 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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Book 2011n 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

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Graph-Theoretic Foundationsograph. The details of the graph-theoretic modeling of specific problems in the context of virtual craniofacial surgery, along with appropriate justifications, will be provided in later chapters. This chapter contains an introductory section, followed by three separate sections on graph matching, graph isomorphism, and network flow.

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