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Proceedings Paper

A denoising algorithm for CT image using low-rank sparse coding
Author(s): Yang Lei; Dong Xu; Zhengyang Zhou; Tonghe Wang; Xue Dong; Tian Liu; Anees Dhabaan; Walter J. Curran; Xiaofeng Yang
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Paper Abstract

We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.

Paper Details

Date Published: 5 March 2018
PDF: 7 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741P (5 March 2018); doi: 10.1117/12.2292890
Show Author Affiliations
Yang Lei, Winship Cancer Institute, Emory Univ. (United States)
Dong Xu, Zhejiang Cancer Hospital (China)
Zhengyang Zhou, Affiliated Hospital of Nanjing Univ. (China)
Tonghe Wang, Winship Cancer Institute, Emory Univ. (United States)
Xue Dong, Winship Cancer Institute, Emory Univ. (United States)
Tian Liu, Winship Cancer Institute, Emory Univ. (United States)
Anees Dhabaan, Winship Cancer Institute, Emory Univ. (United States)
Walter J. Curran, Winship Cancer Institute, Emory Univ. (United States)
Xiaofeng Yang, Winship Cancer Institute, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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