Recent advances in both scanning instruments and supporting software
have transitioned their impact from merely outside the operating room to
inside the surgical theater, making intra-operative 3D imaging a
reality.
This unique text/reference examines the important application of
computer vision and pattern recognition to medical science, with a
specific focus on reconstructive craniofacial surgery. The book
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, which
can also be applied to related fields such as radiology, orthopedic
surgery and histopathology; 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; includes a Foreword by Dr. Jack
C. Yu, Milford B. Hatcher Professor of Surgery at the Medical College of
Georgia, Augusta, GA, USA.
This practical text will be of great resource value to researchers and
graduate students from a broad spectrum of disciplines including
computer science, electrical engineering, biomedical engineering and
statistics. Clinical practitioners such as plastic surgeons, orthopedic
surgeons and radiologists will also find much of interest in the
book.p>
