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Proceedings Paper

An adaptive nonlocal means scheme for medical image denoising
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Paper Abstract

Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. In this work, we investigate an adaptive denoising scheme based on the nonlocal (NL)-means algorithm for medical imaging applications. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means (ANL-means) denoising scheme has three unique features. First, it employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique for robust classification of blocks in noisy images. Second, the local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching algorithm is adopted for better similarity matching. Experimental results from both additive white Gaussian noise (AWGN) and Rician noise are given to demonstrate the superior performance of the proposed ANL denoising technique over various image denoising benchmarks in term of both PSNR and perceptual quality comparison.

Paper Details

Date Published: 12 March 2010
PDF: 12 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76230M (12 March 2010); doi: 10.1117/12.844064
Show Author Affiliations
Tanaphol Thaipanich, The Univ. of Southern California (United States)
C.-C. Jay Kuo, The Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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