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

Shahab Chitchian; Michael Fiddy; Nathaniel M. Fried

doi:10.1117/12.807438

23 February 2009 Denoising during optical coherence tomography of the prostate nerves via bivariate shrinkage using dual-tree complex wavelet transform

Shahab Chitchian, Michael Fiddy, Nathaniel M. Fried

Author Affiliations +

Proceedings Volume 7161, Photonic Therapeutics and Diagnostics V; 716112 (2009) https://doi.org/10.1117/12.807438
Event: SPIE BiOS, 2009, San Jose, California, United States

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%.

Citation Download Citation

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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