Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4968888 | Computer Vision and Image Understanding | 2016 | 18 Pages |
Abstract
Considering vessel segmentation as an iterative tracking process, we propose a new Bayesian tracking algorithm based on particle filters for the delineation of coronary arteries from 3D computed tomography angiograms. It relies on a medial-based geometric model, learned by kernel density estimation, and on a simple, fast and discriminative flux-based image feature. Combining a new sampling scheme and a mean-shift clustering for bifurcation detection and result extraction leads to an efficient and robust method. Results on a database of 61 volumes demonstrate the effectiveness of the proposed approach, with an overall Dice coefficient of 86.2% (and 92.5% on clinically relevant vessels), and a good accuracy of centerline position and radius estimation (errors below the image resolution).
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
David Lesage, Elsa D. Angelini, Gareth Funka-Lea, Isabelle Bloch,