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23 February 2009 Denoising during optical coherence tomography of the prostate nerves via bivariate shrinkage using dual-tree complex wavelet transform
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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%.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shahab Chitchian, Michael Fiddy, and Nathaniel M. Fried "Denoising during optical coherence tomography of the prostate nerves via bivariate shrinkage using dual-tree complex wavelet transform", Proc. SPIE 7161, Photonic Therapeutics and Diagnostics V, 716112 (23 February 2009); https://doi.org/10.1117/12.807438
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