TY - JOUR
T1 - A System for Automated Detection of Ampoule Injection Impurities
AU - Ge, Ji
AU - Xie, Shaorong
AU - Wang, Yaonan
AU - Liu, Jun
AU - Zhang, Hui
AU - Zhou, Bowen
AU - Weng, Falu
AU - Ru, Changhai
AU - Zhou, Chao
AU - Tan, Min
AU - Sun, Yu
PY - 2017/4
Y1 - 2017/4
N2 - 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 ~ 12 ampoule injections per minute 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.
AB - 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 ~ 12 ampoule injections per minute 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.
KW - Ampoule injection inspection
KW - automated ampoule inspection
KW - foreign particles
KW - impurity detection
KW - supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85027693671&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85027693671&origin=recordpage
U2 - 10.1109/TASE.2015.2490061
DO - 10.1109/TASE.2015.2490061
M3 - RGC 21 - Publication in refereed journal
SN - 1545-5955
VL - 14
SP - 1119
EP - 1128
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 2
ER -