Name
Abstract | ||
|---|---|---|
In the field of biomedical imaging analysis on single-cell level, reliable and fast segmentation of the cell nuclei from the background on three-dimensional images is highly needed for the further analysis. In this work we propose an interactive cell segmentation toolkit that first establishes a set of exemplar regions from user input through an easy and intuitive interface in both 2D and 3D in real-time, then extracts the shape and intensity features from those exemplars. Based on a local contrast-constrained region growing scheme, each connected component in the whole image would be filtered by the features from exemplars, forming an “exemplar-matching” group which passed the filtering and would be part of the final segmentation result, and a “non-exemplar-matching” group in which components would be further segmented by the gradient vector field based algorithm. The results of the filtering process are visualized back to the user in near real-time, thus enhancing the experience in exemplar selecting and parameter tuning. The toolkit is distributed as a plugin within the open source Vaa3D system (http://vaa3d.org). |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISBI.2015.7163842 | 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) |
Keywords | Field | DocType |
interactive image segmentation,learning based approach | Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Image texture,Segmentation,Filter (signal processing),Segmentation-based object categorization,Image segmentation,Feature extraction,Artificial intelligence,Region growing | Conference |
ISSN | Citations | PageRank |
1945-7928 | 0 | 0.34 |
References | Authors | |
8 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Li | 1 | 126 | 15.50 |
| Zhi Zhou | 2 | 152 | 10.11 |
| Philipp J. Keller | 3 | 18 | 1.92 |
| Hongkui Zeng | 4 | 14 | 1.97 |
| Tianming Liu | 5 | 1033 | 112.95 |
| Hanchuan Peng | 6 | 3930 | 182.27 |