Automatic plaque assay for the pharmaceutical industry using machine vision

Joseph Wilder; Augustine Tsai; J. M. Festa

doi:10.1117/12.223992

3 October 1995 Automatic plaque assay for the pharmaceutical industry using machine vision

Joseph Wilder, Augustine Tsai, J. M. Festa

Proceedings Volume 2597, Machine Vision Applications, Architectures, and Systems Integration IV; (1995) https://doi.org/10.1117/12.223992
Event: Photonics East '95, 1995, Philadelphia, PA, United States

Abstract

A crucial step in the manufacture of vaccines is the verification of their potency. An assay of the potency must be carried out on every batch produced to determine the safety and efficacy of the vaccine. Currently, human inspectors count the number of plaques (holes) in a cell layer in a petri dish to estimate the potency.They must determine whether nearby plaques that have overgrown each other's borders are single or multiple plaques and distinguish between plaques and small tears in the cell layer resulting from the processing operations (the edges of tears differ in appearance from the edges of plaques). Because of the judgments required to make these subtle distinctions, human inspectors are inconsistent. In cooperation with Merck & Co., Inc., the Rutgers University Center for Computer Aids for Industrial Productivity has demonstrated the feasibility of achieving consistent automatic counting of plaques by a prototype intelligent machine vision system. The David Sarnoff Research Center developed materials handling equipment and factory information system interfaces to enable this prototype system to be installed in a quality control facility at Merck. This paper describes the overall operation of the machine vision aspects of the system, including optics, illumination, sensing, preprocessing, feature extraction and shape recognition. Results of initial tests of the system are also reported.

Citation Download Citation

Joseph Wilder, Augustine Tsai, and J. M. Festa "Automatic plaque assay for the pharmaceutical industry using machine vision", Proc. SPIE 2597, Machine Vision Applications, Architectures, and Systems Integration IV, (3 October 1995); https://doi.org/10.1117/12.223992

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