Name
Title | ||
|---|---|---|
A hybrid active contour model based on global and local information for medical image segmentation |
Abstract | ||
|---|---|---|
For segmenting medical images with abundant noise, blurry boundaries, and intensity heterogeneities effectively, a hybrid active contour model that synthesizes the global information and the local information is proposed. A novel global energy functional is constructed, together with an adaptive weight by the statistical information of image pixels on the clustering idea. Minimizing this global energy functional in a variational level set formulation will drive the curve to desirable boundaries. The local energy functional contains the local threshold, which is used to correct the deviation of the level set function. Experiments demonstrate that the proposed method can segment synthetic and medical images effectively, and have a relatively higher performance compared to other representative methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11045-018-0578-0 | Multidimensional Systems and Signal Processing |
Keywords | Field | DocType |
Medical image segmentation,Active contour,Level set function,Global information,Local information | Active contour model,Mathematical optimization,Market segmentation,Pattern recognition,Global information,Level set,Image segmentation,Pixel,Artificial intelligence,Energy functional,Cluster analysis,Mathematics | Journal |
Volume | Issue | ISSN |
30 | 2 | 1573-0824 |
Citations | PageRank | References |
1 | 0.35 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingling Fang | 1 | 9 | 5.26 |
| Tianshuang Qiu | 2 | 313 | 43.84 |
| Hongyang Zhao | 3 | 69 | 6.09 |
| Fang Lv | 4 | 1 | 0.35 |