CT baggage image enhancement using a combination of alpha-weighted mean separation and histogram equalization

Yicong Zhou; Karen Panetta; Sos Agaian

doi:10.1117/12.849587

28 April 2010 CT baggage image enhancement using a combination of alpha-weighted mean separation and histogram equalization

Yicong Zhou, Karen Panetta, Sos Agaian

Author Affiliations +

Proceedings Volume 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010; 77080G (2010) https://doi.org/10.1117/12.849587
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Baggage scanning systems are used for detecting the presence of explosives and other prohibited items in baggage at security checkpoints in airports. However, the CT baggage images contain projection noise and are of low resolution. This paper introduces a new enhancement algorithm combining alpha-weighted mean separation and histogram equalization to enhance the CT baggage images while removing the background projection noise. A new enhancement measure is introduced for quantitative assessment of image enhancement. Simulations and a comparative analysis are given to demonstrate the new algorithm's performance.

Citation Download Citation

Yicong Zhou, Karen Panetta, and Sos Agaian "CT baggage image enhancement using a combination of alpha-weighted mean separation and histogram equalization", Proc. SPIE 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010, 77080G (28 April 2010); https://doi.org/10.1117/12.849587

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