Name
Abstract | ||
|---|---|---|
We propose a Bayesian formulation for coupled surface evolutions and apply it to the segmentation of the prostate and the bladder in CT images. This is of great interest to the radiotherapy treatment process, where an accurate contouring of the prostate and its neighboring organs is needed. A purely data based approach fails, because the prostate boundary is only partially visible. To resolve this issue, we define a Bayesian framework to impose a shape constraint on the prostate, while coupling its extraction with that of the bladder. Constraining the segmentation process makes the extraction of both organs’ shapes more stable and more accurate. We present some qualitative and quantitative results on a few data sets, validating the performance of the approach. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11569541_26 | CVBIA |
Keywords | Field | DocType |
great interest,surface evolution,bladder segmentation,neighboring organ,bayesian framework,accurate contouring,radiotherapy treatment process,ct image,segmentation process,bayesian formulation,prostate boundary | Computer vision,Data set,Pattern recognition,Segmentation,Computer science,Artificial intelligence,Prostate,Contouring,Bayesian formulation,Bayesian probability | Conference |
ISBN | Citations | PageRank |
3-540-29411-2 | 18 | 1.23 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mikael Rousson | 1 | 1002 | 41.09 |
| Ali Khamene | 2 | 469 | 38.63 |
| Mamadou Diallo | 3 | 36 | 3.58 |
| Juan Carlos Celi | 4 | 18 | 1.91 |
| Frank Sauer | 5 | 145 | 11.04 |