Graphical Shape Templates for Deformable Model Registration with Applications to MRI Brain Scans.

Abstract

A new method of template matching is proposed using graphical templates. A 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 decomposable subgraphs of the template graph finds the optimal match to a subset of the candidate points in polynomial time. This combination of local operators to describe points of interest/landmarks and a graph to describe their geometric orientation in the plane, yields fast and precise matches of the model to the data, with no initialization required. In addition it provides a generic toolbox for modeling shape in a variety of applications. This methodology is applied in the context of T2 weighted MR images of the brain.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1995
Accession Number
ADA290705

Entities

People

  • Yali Amat

Organizations

  • University of Chicago

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Computer Vision
  • Computers
  • Diagnostic Imaging
  • Dynamic Programming
  • Identification
  • Image Processing
  • Image Restoration
  • Mathematics
  • Models
  • Object Recognition
  • Polynomials
  • Recognition
  • Sequence Analysis
  • Shape
  • Template Patterns

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Computer Vision.
  • Graph Algorithms and Convex Optimization.