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Proceedings Paper

CT image noise reduction using rotational-invariant feature in Stockwell transform
Author(s): Jian Su; Zhoubo Li; Lifeng Yu; Joshua Warner; Daniel Blezek; Bradley Erickson
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Paper Abstract

Iterative reconstruction and other noise reduction methods have been employed in CT to improve image quality and to reduce radiation dose. The non-local means (NLM) filter emerges as a popular choice for image-based noise reduction in CT. However, the original NLM method cannot incorporate similar structures if they are in a rotational format, resulting in ineffective denoising in some locations of the image and non-uniform noise reduction across the image. We have developed a novel rotational-invariant image texture feature derived from the multiresolutional Stockwell-transform (ST), and applied it to CT image noise reduction so that similar structures can be identified and fully utilized even when they are in different orientations. We performed a computer simulation study in CT to demonstrate better efficiency in terms of utilizing redundant information in the image and more uniform noise reduction achieved by ST than by NLM.

Paper Details

Date Published: 21 March 2014
PDF: 5 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903433 (21 March 2014); doi: 10.1117/12.2044360
Show Author Affiliations
Jian Su, Mayo Clinic (United States)
Zhoubo Li, Mayo Clinic (United States)
Lifeng Yu, Mayo Clinic (United States)
Joshua Warner, Mayo Clinic (United States)
Daniel Blezek, Mayo Clinic (United States)
Bradley Erickson, Mayo Clinic (United States)

Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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