A System for Automated Detection of Ampoule Injection Impurities

doi:10.1109/TASE.2015.2490061

	A System for Automated Detection of Ampoule Injection Impurities
	Ge, Ji 1,2; Xie, Shaorong 3; Wang, Yaonan 4; Liu, Jun 5; Zhang, Hui6 ; Zhou, Bowen 7; Weng, Falu 2; Ru, Changhai 8,9,10; Zhou, Chao11 ; Tan, Min11 ; Sun, Yu 1
发表期刊	IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
	2017-04-01
卷号	14 期号:2 页码:1119-1128
文章类型	Article
摘要	Ampoule injection is a routinely used treatment in hospitals due to its rapid effect after intravenous injection. During manufacturing, tiny foreign particles can be present in the ampoule injection. Therefore, strict inspection must be performed before ampoule injections can be sold for hospital use. In the quality control inspection process, most ampoule enterprises still rely on manual inspection which suffers from inherent inconsistency and unreliability. This paper reports an automated system for inspecting foreign particles within ampoule injections. A custom-designed hardware platform is applied for ampoule transportation, particle agitation, and image capturing and analysis. Constructed trajectories of moving objects within liquid are proposed for use to differentiate foreign particles from air bubbles and random noise. To accurately classify foreign particles, multiple features including particle area, mean gray value, geometric invariant moments, and wavelet packet energy spectrum are used in supervised learning to generate feature vectors. The results show that the proposed algorithm is effective in classifying foreign particles and reducing false positive rates. The automated inspection system inspects over 150 ampoule injections per minute (versus by technologist) with higher accuracy and repeatability. In addition, the automated system is capable of diagnosing impurity types while existing inspection systems are not able to classify detected particles.
关键词	Ampoule Injection Inspection Automated Ampoule Inspection Foreign Particles Impurity Detection Supervised Learning
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TASE.2015.2490061
关键词[WOS]	SUPPORT VECTOR MACHINES ; PARTICLE INSPECTION ; NETWORKS ; TRACKING
收录类别	SCI
语种	英语
项目资助者	Canada Research Chairs Program ; National Natural Science Foundation of China(61305019 ; Natural Science Foundation of Jiangxi Province(20132BAB211032 ; Shanghai Municipal Science and Technology Commission(14JC1491500) ; International S&T Cooperation Program of China(2014DFA70470) ; 61463018 ; 20151BAB207046 ; 61401046 ; GJJ13385) ; 61528304)
WOS研究方向	Automation & Control Systems
WOS类目	Automation & Control Systems
WOS记录号	WOS:000399347500063
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15095
专题	复杂系统认知与决策实验室_先进机器人
作者单位	1.Univ Toronto, Adv Micro & Nanosyst Lab, Toronto, ON M5S 3G8, Canada 2.Jiangxi Univ Sci & Technol, Coll Elect Engn & Automat, Ganzhou 341000, Peoples R China 3.Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China 4.Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China 5.Univ Toronto, Adv Micro & Nanosyst Lab, Toronto, ON M5S 3G8, Canada 6.Changsha Univ Sci & Technol, Coll Elect & Informat Engn, Changsha 410012, Hunan, Peoples R China 7.Hunan Univ Sci & Technol, Dept Elect Engn, Xiangtan 411201, Peoples R China 8.Soochow Univ, Res Ctr Robot & Micro Syst, Suzhou 215021, Peoples R China 9.Soochow Univ, Collaborat Innovat Ctr Suzhou Nano Sci & Technol, Suzhou 215021, Peoples R China 10.Harbin Engn Univ, Coll Automat, Harbin 150001, Peoples R China 11.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ge, Ji,Xie, Shaorong,Wang, Yaonan,et al. A System for Automated Detection of Ampoule Injection Impurities[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,2017,14(2):1119-1128.
APA	Ge, Ji.,Xie, Shaorong.,Wang, Yaonan.,Liu, Jun.,Zhang, Hui.,...&Sun, Yu.(2017).A System for Automated Detection of Ampoule Injection Impurities.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,14(2),1119-1128.
MLA	Ge, Ji,et al."A System for Automated Detection of Ampoule Injection Impurities".IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 14.2(2017):1119-1128.