Name
Title | ||
|---|---|---|
Active contour model driven by global and local intensity information for ultrasound image segmentation. |
Abstract | ||
|---|---|---|
Due to the abundant noise, blurry boundaries, and intensity inhomogeneities present in ultrasound (US) images, it is a difficult task to segment US images accurately. In this paper, we propose a novel active contour method that combines global and local region information to achieve this task. The global information can segment US images with noise and blurry boundaries; while the local information can settle the intensity homogeneities of images. The proposed method can be directly applied to synthetic, real, and US-images segmentation. Results demonstrate the superiority of the proposed method over other representative algorithms. Moreover, we also extend the proposed method to vector-valued images. Experiments are performed to testify the feasibility of the method, and the proposed vector-valued idea can be applied to the medical co-segmentation in the future. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.camwa.2018.03.029 | Computers & Mathematics with Applications |
Keywords | Field | DocType |
Ultrasound image segmentation,Active contour,Partial differential equations,Vector image segmentation | Active contour model,Computer vision,Mathematical optimization,Segmentation,Global information,Ultrasound image segmentation,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
75 | 12 | 0898-1221 |
Citations | PageRank | References |
1 | 0.35 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingling Fang | 1 | 9 | 5.26 |
| Tianshuang Qiu | 2 | 313 | 43.84 |
| Yin Liu | 3 | 1 | 0.35 |
| Chaofeng Chen | 4 | 1 | 0.35 |