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A database for assessment of effect of lossy compression on digital mammograms
Author(s): Jiheng Wang; Berkman Sahiner; Nicholas Petrick; Aria Pezeshk
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Paper Abstract

With widespread use of screening digital mammography, efficient storage of the vast amounts of data has become a challenge. While lossless image compression causes no risk to the interpretation of the data, it does not allow for high compression rates. Lossy compression and the associated higher compression ratios are therefore more desirable. The U.S. Food and Drug Administration (FDA) currently interprets the Mammography Quality Standards Act as prohibiting lossy compression of digital mammograms for primary image interpretation, image retention, or transfer to the patient or her designated recipient. Previous work has used reader studies to determine proper usage criteria for evaluating lossy image compression in mammography, and utilized different measures and metrics to characterize medical image quality. The drawback of such studies is that they rely on a threshold on compression ratio as the fundamental criterion for preserving the quality of images. However, compression ratio is not a useful indicator of image quality. On the other hand, many objective image quality metrics (IQMs) have shown excellent performance for natural image content for consumer electronic applications. In this paper, we create a new synthetic mammogram database with several unique features. We compare and characterize the impact of image compression on several clinically relevant image attributes such as perceived contrast and mass appearance for different kinds of masses. We plan to use this database to develop a new objective IQM for measuring the quality of compressed mammographic images to help determine the allowed maximum compression for different kinds of breasts and masses in terms of visual and diagnostic quality.

Paper Details

Date Published: 7 March 2018
PDF: 12 pages
Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 1057706 (7 March 2018); doi: 10.1117/12.2293828
Show Author Affiliations
Jiheng Wang, U.S. Food and Drug Administration (United States)
Berkman Sahiner, U.S. Food and Drug Administration (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)
Aria Pezeshk, U.S. Food and Drug Administration (United States)


Published in SPIE Proceedings Vol. 10577:
Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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