Knowledge-Based 3D Segmentation and Reconstruction of Coronary Arteries Using CT Images

Abstract

An approach for the 3D segmentation and reconstruction of human left coronary arteries using angio-CT images is presented in this paper. Each voxel in the 3D dataset is assumed to belong to one of the three homogeneous regions: blood, myocardium, and lung. A priori knowledge of the regions is introduced via Bayes rule. Posterior probabilities obtained using Bayes rule are anisotropically smoothed, and the 3D segmentation is obtained via MAP classifications of the smoothed posteriors. An active contour model is then applied to extract the coronary arteries from the rest of the volumetric data with subvoxel accuracy. The geometric model of the left coronary arteries obtained in this work may be used to provide accurate boundary conditions for hemodynamic simulations, or to provide objective measurements of clinically relevant parameters such as lumen sizes in a 3D sense.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2004
Accession Number
ADA465805

Entities

People

  • Allen Tannenbaum
  • Don Giddens
  • Yan Yang

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Biomedical
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Angiography
  • Arteries
  • Biomedical Engineering
  • Blood
  • Boundaries
  • Classification
  • Computational Fluid Dynamics
  • Computer Vision
  • Computers
  • Data Sets
  • Engineering
  • Fluid Dynamics
  • High Resolution
  • Imaging Techniques
  • Probability
  • Standards

Fields of Study

  • Medicine
  • Physics

Readers

  • Cardiovascular Physiology
  • Computer Vision.