An effective system for optical microscopy cell image segmentation, tracking and cell phase identification

被引:6
作者
Yan, Jun [1 ,2 ,4 ]
Zhou, Xiaobo [2 ,3 ]
Yang, Qiong [4 ]
Liu, Ning [2 ,4 ]
Cheng, Qiansheng [1 ]
Wong, Stephen T. C. [2 ,3 ]
机构
[1] Peking Univ, Sch Math Sci, LMAM, Beijing 100871, Peoples R China
[2] Harvard Med Sch, HCNR, Ctr Bioinformat, Boston, MA 02115 USA
[3] Brigham & Womens Hosp, HMS, Mol Imaging Ctr, Boston, MA 02115 USA
[4] Microsoft Res Asia, Beijing 100080, Peoples R China
来源
2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS | 2006年
关键词
biomedical image processing; biological systems; biomedical signal processing;
D O I
10.1109/ICIP.2006.313143
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The lacking of automatic screen systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modem bio-imaging research. In this paper, we propose an effective automated analytic system that can be used to acquire, track and analyze cell-cycle behaviors of a large population of cells. We use traditional watershed algorithm for cell nuclei segmentation and then a novel hybrid merging method is proposed for fragments merging. After a distance and size based tracking procedure, the performance of fragments merging is improved again by the sequence context information. At last, the cell nuclei can be classified into different phases accurately in a continuous Hidden Markov Model (HMM). Experimental results show the proposed system is very effective for cell sequence segmentation, tracking and cell phase identification.
引用
收藏
页码:1917 / +
页数:2
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