Graphical shape templates for automatic anatomy detection with applications to MRI brain scans.
A new method of model registration is proposed using graphical templates. A decomposable graph of landmarks is chosen in the template image. All possible candidates for these landmarks are found in the data image using robust relational local operators. A dynamic programming algorithm on the template graph finds the optimal match to a subset of the candidate points in polynomial time. This combination--local operators to describe points of interest/landmarks and a graph to describe their geometric arrangement in the plane--yields fast and precise matches of the model to the data with no initialization required. In addition, it provides a generic tool box for modeling shape in a variety of applications. This methodology is applied in the context of T2-weighted magnetic resonance (MR) axial and sagittal images of the brain to identify specific anatomies.
Amit
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Y
Department of Statistics, University of Chicago, IL 60637, USA. amit@galton.uchicago.edu
D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology*; D002552:Cerebral Ventricles / Q000033:anatomy & histology; D003196:Computer Graphics; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000145:classification / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D012984:Software