Name
Abstract | ||
|---|---|---|
Due to various and unpredictable challenges occurring in studying the cycle of small size of multiple cells, such as varying number of cell population, cell morphological variation, and cell irregular motion, a particle swarm optimization (PSO) based approach is proposed for automatic estimation of biological cells' contours and positions. The proposed approach is divided into two steps, i.e., the stage of approximate position estimation and the stage of accurate contour estimation of multiple cells, which are implemented by the PSO-based tracking module, PSO-based discovery module, and PSO-based contour module, respectively. The tracking procedure is tested over real cell image sequences and is shown to provide high accuracy both in position and contour estimations of each cell in various challenging cases. Furthermore, it is more competitive against the state-of-the-art multi-object tracking methods in terms of performance measures such as FAR, FNR, LTR, and LSR. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICCAIS.2014.7020569 | ICCAIS |
Keywords | DocType | ISSN |
cell morphological variation,cellular biophysics,cell image sequences,multicell multiparameter estimation,cell irregular motion,biological cell contour estimation,far measure,particle swarm optimisation,biological cell position estimation,fnr measure,pso-based contour module,pso-based tracking module,multiobject tracking method,ltr measure,pso-based discovery module,biology computing,object tracking,image sequences,lsr measure,cell population,pso-based approach,tracking procedure,particle swarm optimization | Conference | 2475-7896 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benlian Xu | 1 | 81 | 20.96 |
| Yayun Ren | 2 | 0 | 0.34 |
| Peiyi Zhu | 3 | 21 | 5.78 |
| Mingli Lu | 4 | 36 | 11.58 |