Name
Abstract | ||
|---|---|---|
We present an approach for segmenting left ventricular endocardial boundaries from RF ultrasound. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model. The conditional model relates neighboring frames in the image sequence by means of a computationally efficient linear predictor that exploits spatio-temporal coherence in the data. Segmentation using the RF data overcomes problems due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from 6 canine studies. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22092-0_4 | IPMI |
Field | DocType | Volume |
Computer vision,Scale-space segmentation,Spatial model,Pattern recognition,Speckle pattern,Segmentation,Computer science,Level set,Linear prediction,Coherence (physics),Robustness (computer science),Artificial intelligence | Conference | 22 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul C. Pearlman | 1 | 7 | 2.23 |
| Hemant D. Tagare | 2 | 485 | 58.76 |
| Ben A. Lin | 3 | 54 | 6.34 |
| Albert J. Sinusas | 4 | 521 | 49.46 |
| James S. Duncan | 5 | 2973 | 466.48 |