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Proceedings Paper

Denoising during optical coherence tomography of the prostate nerves via bivariate shrinkage using dual-tree complex wavelet transform
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Paper Abstract

The performance of wavelet shrinkage algorithms for image-denoising can be improved significantly by considering the statistical dependencies among wavelet coefficients as demonstrated by several algorithms presented in the literature. In this paper, a locally adaptive denoising algorithm using a bivariate shrinkage function is applied to reduce speckle noise in time-domain (TD) optical coherence tomography (OCT) images of the prostate. The algorithm is illustrated using the dual-tree complex wavelet transform. The cavernous nerve and prostate gland can be separated from discontinuities due to noise, and image quality metrics improvements with signal-to-noise ratio (SNR) increase of 14 dB are attained with a sharpness reduction of only 3%.

Paper Details

Date Published: 23 February 2009
PDF: 4 pages
Proc. SPIE 7161, Photonic Therapeutics and Diagnostics V, 716112 (23 February 2009); doi: 10.1117/12.807438
Show Author Affiliations
Shahab Chitchian, The Univ. of North Carolina at Charlotte (United States)
Michael Fiddy, The Univ. of North Carolina at Charlotte (United States)
Nathaniel M. Fried, The Univ. of North Carolina at Charlotte (United States)
Johns Hopkins Medical Institutions (United States)

Published in SPIE Proceedings Vol. 7161:
Photonic Therapeutics and Diagnostics V
Henry Hirschberg; Brian Jet-Fei Wong; Kenton W. Gregory; Reza S. Malek; Nikiforos Kollias; Bernard Choi; Guillermo J. Tearney; Justus F. R. Ilgner; Steen J. Madsen; Laura Marcu; Haishan Zeng, Editor(s)

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