Name
Abstract | ||
|---|---|---|
We present an approach for segmenting the left ventricular endocardial boundaries from radio-frequency (RF) ultrasound. The method employs a computationally efficient two-frame linear predictor which exploits the spatio-temporal coherence of the data. By performing segmentation using the RF data we are able to overcome problems due to image inhomogeneities that are often amplified in B-mode segmentation, as well as provide geometric constraints for RF phase-based speckle tracking. We illustrate the advantages of our approach by comparing it to manual tracings of B-mode data and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 28 3D sequences acquired from 6 canine studies, imaged both at baseline and 1 hour post infarction. |
Year | Venue | Keywords |
|---|---|---|
2010 | MICCAI | ultrasound,algorithms,radio waves,computer simulation,linear models,level set,myocardial infarction,radio frequency |
Field | DocType | Volume |
Active contour model,Computer vision,Pattern recognition,Speckle pattern,Linear model,Segmentation,Computer science,Level set,Radio frequency,Linear prediction,Coherence (physics),Artificial intelligence | Conference | 13 |
Issue | ISSN | ISBN |
Pt 1 | 0302-9743 | 3-642-15704-1 |
Citations | PageRank | References |
3 | 0.44 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul C. Pearlman | 1 | 7 | 2.23 |
| Hemant D. Tagare | 2 | 485 | 58.76 |
| Albert J. Sinusas | 3 | 521 | 49.46 |
| James S. Duncan | 4 | 2973 | 466.48 |