Name
Abstract | ||
|---|---|---|
Design of a geometric vascular model.Non-parametric Bayesian model, learned by kernel density estimation from manually segmented datasets.Design of a new sampling scheme, Adaptive Auxiliary Particle Filtering (AAPF).Mean-Shift clustering for bifurcation detection and coronary tree extraction, and high computational efficiency.Experiments demonstrate the robustness of the proposed approach for complete vessel tree segmentation. 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). |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.cviu.2015.11.009 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
3D CTA,Coronary segmentation,Bifurcations,Tracking with particle filter,Bayesian model,Geometric model,Flux feature,Kernel estimation,Sampling scheme,Mean-shift | Computer vision,Pattern recognition,Sørensen–Dice coefficient,Segmentation,Geometric modeling,Particle filter,Robustness (computer science),Artificial intelligence,Mean-shift,Cluster analysis,Mathematics,Kernel density estimation | Journal |
Volume | Issue | ISSN |
151 | C | 1077-3142 |
Citations | PageRank | References |
7 | 0.54 | 38 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Lesage | 1 | 441 | 18.16 |
| Elsa D. Angelini | 2 | 740 | 60.44 |
| Gareth Funka-Lea | 3 | 1383 | 63.84 |
| Isabelle Bloch | 4 | 2123 | 170.75 |