Name
Abstract | ||
|---|---|---|
Recent advances in 4D imaging and real-time imaging provide image data with clinically important cardiac dynamic information at high spatial or temporal resolution. However, the enormous amount of information contained in these data has also raised a challenge for traditional image analysis algorithms in terms of efficiency. In this paper, a novel deformable model framework, Active Geometric Functions (AGF), is introduced to tackle the real-time segmentation problem. As an implicit framework paralleling to level-set, AGF has mathematical advantages in efficiency and computational complexity as well as several flexible feature similar to level-set framework. AGF is demonstrated in two cardiac applications: endocardial segmentation in 4D ultrasound and myocardial segmentation in MRI with super high temporal resolution. In both applications, AGF can perform real-time segmentation in several milliseconds per frame, which was less than the acquisition time per frame. Segmentation results are compared to manual tracing with comparable performance with inter-observer variability. The ability of such real-time segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.cmpb.2009.09.001 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
real-time segmentation,interactive image acquisition,real-time segmentation problem,segmentation result,real-time imaging,implicit framework,active geometric functions,cardiac imaging,active geometric functions agf,deformable model,online segmentation,image data,endocardial segmentation,myocardial segmentation,ultrasound,algorithms,level set,image analysis,temporal resolution,real time,computational complexity,heart | Computer vision,Scale-space segmentation,Computer science,Segmentation,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Temporal resolution,Tracing,Computational complexity theory | Journal |
Volume | Issue | ISSN |
98 | 3 | 1872-7565 |
Citations | PageRank | References |
21 | 0.89 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Duan | 1 | 80 | 7.58 |
| Elsa D. Angelini | 2 | 740 | 60.44 |
| Andrew F. Laine | 3 | 747 | 83.01 |