Name
Title | ||
|---|---|---|
Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region Growing and Supervised Edge-Based Deformable Model. |
Abstract | ||
|---|---|---|
Automated 3-D breast ultrasound has been proposed as a complementary modality to mammography for early detection of breast cancers. To facilitate the interpretation of these images, computer aided detection systems are being developed in which mass segmentation is an essential component for feature extraction and temporal comparisons. However, automated segmentation of masses is challenging becaus... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMI.2017.2787685 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Image segmentation,Deformable models,Ultrasonic imaging,Breast cancer,Shape | Breast ultrasound,Mammography,Computer vision,Segmentation,Level set,Feature extraction,Image segmentation,Artificial intelligence,Region growing,Mathematics,Mixture model | Journal |
Volume | Issue | ISSN |
37 | 4 | 0278-0062 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ehsan Kozegar | 1 | 2 | 1.37 |
| soryani | 2 | 29 | 9.48 |
| Hamid Behnam | 3 | 43 | 9.94 |
| M. Salamati | 4 | 1 | 0.34 |
| Tao Tan | 5 | 46 | 10.25 |