Name
Abstract | ||
|---|---|---|
This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells ( objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1155/2015/695054 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Field | DocType | Volume |
Computer vision,Cell movement,Cell tracking,Computer science,Particle filter,Collision,Artificial intelligence,Dynamic models,Ambiguity,Machine learning | Journal | 2015 |
ISSN | Citations | PageRank |
1748-670X | 1 | 0.37 |
References | Authors | |
24 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingli Lu | 1 | 36 | 11.58 |
| Benlian Xu | 2 | 81 | 20.96 |
| Andong Sheng | 3 | 32 | 9.94 |
| Zhengqiang Jiang | 4 | 2 | 0.72 |
| Liping Wang | 5 | 2 | 0.72 |
| Peiyi Zhu | 6 | 2 | 1.39 |
| Jian Shi | 7 | 18 | 4.78 |