A System for Automated Detection of Ampoule Injection Impurities

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

11 Scopus Citations
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Author(s)

  • Ji Ge
  • Shaorong Xie
  • Yaonan Wang
  • Hui Zhang
  • Bowen Zhou
  • Falu Weng
  • Changhai Ru
  • Chao Zhou
  • Min Tan
  • Yu Sun

Detail(s)

Original languageEnglish
Pages (from-to)1119-1128
Journal / PublicationIEEE Transactions on Automation Science and Engineering
Volume14
Issue number2
Online published4 Nov 2015
Publication statusPublished - Apr 2017
Externally publishedYes

Abstract

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.

Research Area(s)

  • Ampoule injection inspection, automated ampoule inspection, foreign particles, impurity detection, supervised learning

Citation Format(s)

A System for Automated Detection of Ampoule Injection Impurities. / Ge, Ji; Xie, Shaorong; Wang, Yaonan et al.

In: IEEE Transactions on Automation Science and Engineering, Vol. 14, No. 2, 04.2017, p. 1119-1128.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review