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Proceedings Paper

Adaptive trimmed mean filter for computed tomographic imaging
Author(s): Jiang Hsieh
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Paper Abstract

The image quality of a computed tomography (CT) scan is frequently degraded by severe streaking artifacts resulting from excessive x-ray quantum noise. When this occurs, a patient has to be re-scanned at a higher x-ray technique to obtain an acceptable image for diagnosis. This approach results in not only unnecessary dosage to the patient, but also a delayed patient diagnosis and a reduced patient throughput. In this paper, we propose an adaptive trimmed mean filter (ATMF) in Radon space to combat this problem. The ATMF is an extension to the existing (alpha) -inner mean filter in that both sample size M and the trimming parameter (alpha) are selected based on the local statistics. In addition, the 2D ATMF is unsymmetrical to adapt to the sampling pattern in Radon pace. Phantom studies and clinical evaluations have shown that this type of filter is very effective in reducing or eliminating quantum noise induced artifacts. At the same time, the impact on the image spatial resolution has been kept to a minimum.

Paper Details

Date Published: 8 July 1994
PDF: 9 pages
Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); doi: 10.1117/12.179262
Show Author Affiliations
Jiang Hsieh, General Electric Medical Systems (United States)

Published in SPIE Proceedings Vol. 2299:
Mathematical Methods in Medical Imaging III
Fred L. Bookstein; James S. Duncan; Nicholas Lange; David C. Wilson, Editor(s)

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