Name
Title | ||
|---|---|---|
Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation |
Abstract | ||
|---|---|---|
Edge-based active contour models are effective in segmenting images with intensity inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as in medical images. Traditional edge-stop functions (ESFs) utilize only gradient information, which fails to stop contour evolution at such boundaries because of the small gradient magnitudes. To address this problem, we propose a framework to construct a group of ESFs for edge-based active contour models to segment objects with poorly defined boundaries. In our framework, which incorporates gradient information as well as probability scores from a standard classifier, the ESF can be constructed from any classification algorithm and applied to any edge-based model using a level set method. Experiments on medical images using the distance regularized level set for edge-based active contour models as well as the k-nearest neighbours and the support vector machine confirm the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LSP.2015.2508039 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
Edge-based active contour,edge-stop function,gradient information,image segmentation,probability score | Active contour model,Computer vision,Image gradient,Pattern recognition,Level set method,Support vector machine,Level set,Image segmentation,Artificial intelligence,Classifier (linguistics),Morphological gradient,Mathematics | Journal |
Volume | Issue | ISSN |
23 | 2 | 1070-9908 |
Citations | PageRank | References |
16 | 0.69 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Agus Pratondo | 1 | 23 | 1.85 |
| Chee-Kong Chui | 2 | 245 | 38.34 |
| Sim Heng Ong | 3 | 426 | 44.63 |