Name
Abstract | ||
|---|---|---|
Many biomedical applications require detection of curvilinear structures in images and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here, we propose a contrast-independent approach to identify curvilinear structures based on oriented phase congruency, i.e., the phase congruency tensor (PCT). We show that the proposed method is largely insensitive to intensity variations along the curve and provides successful detection within noisy regions. The performance of the PCT is evaluated by comparing it with state-of-the-art intensity-based approaches on both synthetic and real biological images. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TIP.2012.2185938 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
image segmentation,medical image processing,object detection,PCT,biomedical applications,biomedical image detection,contrast-independent curvilinear structure detection,phase congruency tensor,semiautomatic segmentation,Bioimage informatics,curvilinear structure,live-wire tracing,phase congruency tensor (PCT) | Object detection,Computer vision,Pattern recognition,Tensor,Computer science,Segmentation,Feature extraction,Image segmentation,Curvilinear coordinates,Artificial intelligence,Bioimage informatics,Phase congruency | Journal |
Volume | Issue | ISSN |
21 | 5 | 1941-0042 |
Citations | PageRank | References |
5 | 0.44 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boguslaw Obara | 1 | 145 | 17.81 |
| Mark Fricker | 2 | 25 | 3.65 |
| David Gavaghan | 3 | 213 | 30.44 |
| Vicente Grau | 4 | 20 | 1.94 |