Journal of South China University of Technology (Natural Science Edition) ›› 2014, Vol. 42 ›› Issue (3): 15-20,34.doi: 10.3969/j.issn.1000-565X.2014.03.003

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Cell Segmentation in the Process of Cell Microinjection Based on Variation Level Set

Qin Chuan- bo1 Du Qi- liang1,2 Tian Lian- fang1 Zhang Qin3   

  1. 1.School of Automation Science and Engineering，South China University of Technology,Guangzhou 510640,Guangdong,China;2.Key Laboratory of Autonomous System and Network Control of the Ministry of Education,Guangzhou 510640,Guangdong,China;3.School of Mechanical and Automotive Engineering,South China University of Technology, Guangzhou 510640,Guangdong,China
  • Received:2013-10-30 Online:2014-03-25 Published:2014-02-19
  • Contact: 杜启亮(1980-)，男，副研究员，主要从事微小机器人、视觉控制研究． E-mail:qldu@scut.edu.cn
  • About author:秦传波(1982-)，男，博士生，主要从事模式识别与智能系统研究．E-mail:tenround@163．com
  • Supported by:

    广东省科技厅国际合作项目(2012B050600011);广东高校国际科技合作项目(粤教科函［ 2010］ 119);高等学校博士学科点专项科研基金新教师课题(20120172120032);广州市科信局国际合作项目(2012J5100001)

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Abstract:

So far as the cytoplasm segmentation in the process of cell microinjection is concerned,the traditionallevel set models can not directly segment cytoplasm from multiple adhered targets.Additionally,they are slow insegmentation.In order to solve these problems,a new cell segmentation method based on variation level set is pro-posed.In this method,the joint contour of cell and injection pipette is acquired according to the difference inshape.Then,by combining the variation level set theory,an extra edge constraint is added to improve the energyfunctional model.A complete cell segmentation is thus achieved and the convergence speed of the variation level setis significantly improved.Experimental results indicate that the proposed method helps to perform the independentcytoplasm segmentation rapidly and at the same time avoid wrong segmentation of other targets.

Key words: variation level set, automatic cell injection, cell segmentation, image enhancement