Name
Abstract | ||
|---|---|---|
Robust initialization is essential for any successful segmentation process of medical images. For CT images, initialization is challenging because quality, appearance, content, and field-of-view of the images are highly variable. Furthermore, high execution speed is desirable, whereas the user tolerance to errors is low in clinical applications. We present a new method for efficient and robust positioning of organs in CT images. It is based on a novel probabilistic atlas that, given a tissue type, describes the probability density of the random variable spatial location. Random sampling is then employed to select the most informative points for matching. We present results on pelvic and abdominal images acquired for radiotherapy planning. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISBI.2012.6235553 | Biomedical Imaging |
Keywords | Field | DocType |
biological organs,biological tissues,computerised tomography,image matching,image sampling,medical image processing,probability,radiation therapy,random processes,CT image,abdominal image,biological tissue,clinical applications,image matching,medical image segmentation process,pelvic image,probabilistic atlas,probability density,radiotherapy planning,random sampling,random variable spatial location,robust organ positioning,CT,atlas-image registration,probabilistic registration,radiotherapy | Computer vision,Random variable,Pattern recognition,Segmentation,Computer science,Stochastic process,Image segmentation,Robustness (computer science),Artificial intelligence,Probabilistic logic,Initialization,Probability density function | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-4577-1857-1 | 2 |
PageRank | References | Authors |
0.45 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vik, T. | 1 | 3 | 2.17 |
| Bystrov, D. | 2 | 2 | 0.45 |
| Schadewaldt, N. | 3 | 2 | 0.45 |
| Heinrich Schulz | 4 | 9 | 3.69 |