Application of adaptive texture filters to the automated visual inspection of antibiotic susceptibility tests

David Zhengwen Zhang; Simon Snowden; Jia-Chang Wang; David Kind

doi:10.1117/12.332805

17 December 1998 Application of adaptive texture filters to the automated visual inspection of antibiotic susceptibility tests

David Zhengwen Zhang, Simon Snowden, Jia-Chang Wang, David Kind

Author Affiliations +

Proceedings Volume 3518, Sensors and Controls for Intelligent Machining, Agile Manufacturing, and Mechatronics; (1998) https://doi.org/10.1117/12.332805
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

Abstract

Computer vision systems have been used in recent years to perform automated antibiotic susceptibility test based on the disk-diffusion method. However, certain organisms do not reflect light very well. As such, the reliability of such automated inspection systems is sometimes not as high as expected. This paper proposes to use texture analysis to improve the quality of test images and thereby simplify the inspection tasks. Adaptive texture filters are used to maximize the difference between regions of interest in a test image and the background, enabling a thresholding operation to be carried out easily. The principles of adaptive filtering for texture analysis are discussed. A training algorithm is presented to generate optimized filters for generic texture inspection problems. An experimental study is carried out to investigate the performance of this technique in highlighting poorly reflecting organisms in antibiotic susceptibility testes.

Citation Download Citation

David Zhengwen Zhang, Simon Snowden, Jia-Chang Wang, and David Kind "Application of adaptive texture filters to the automated visual inspection of antibiotic susceptibility tests", Proc. SPIE 3518, Sensors and Controls for Intelligent Machining, Agile Manufacturing, and Mechatronics, (17 December 1998); https://doi.org/10.1117/12.332805

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available