Accession Number:

ADA465805

Title:

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

Descriptive Note:

Corporate Author:

GEORGIA INST OF TECH ATLANTA DEPT OF BIOMEDICAL ENGINEERING

Report Date:

2004-09-01

Pagination or Media Count:

4.0

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.

Subject Categories:

  • Anatomy and Physiology
  • Medicine and Medical Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE