Name
Title | ||
|---|---|---|
Attributed Relational Graphs for Cell Nucleus Segmentation in Fluorescence Microscopy Images |
Abstract | ||
|---|---|---|
More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TMI.2013.2255309 | Medical Imaging, IEEE Transactions |
Keywords | Field | DocType |
biomedical optical imaging,cellular biophysics,fluorescence,graph theory,image segmentation,medical image processing,molecular biophysics,monolayers,optical microscopy,attributed relational graphs,automated microscopy imaging,cell nucleus segmentation,fluorescence microscopy image,model-based nucleus segmentation algorithm,molecular cellular biology,monolayer isolated cells,nucleus boundaries,nucleus identification problem,predefined structural patterns,spatial relations,Attributed relational graph,fluorescence microscopy imaging,graph,model-based segmentation,nucleus segmentation | Graph theory,Spatial relation,Computer vision,Nucleus,Open problem,Segmentation,Image segmentation,Molecular biophysics,Artificial intelligence,Region growing,Mathematics | Journal |
Volume | Issue | ISSN |
32 | 6 | 0278-0062 |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salim Arslan | 1 | 52 | 5.19 |
| Tulin Ersahin | 2 | 5 | 1.45 |
| Rengül Çetin-atalay | 3 | 77 | 12.63 |
| Cigdem Gunduz-Demir | 4 | 47 | 3.69 |
| Cetin-Atalay, R. | 5 | 3 | 0.39 |
| Gunduz-Demir, C. | 6 | 3 | 0.39 |