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

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

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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%.

Original language	English (US)
Title of host publication	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	7161
DOIs	https://doi.org/10.1117/12.807438
State	Published - 2009
Event	Photonic Therapeutics and Diagnostics V - San Jose, CA, United States Duration: Jan 24 2009 → Jan 26 2009

Other

Other	Photonic Therapeutics and Diagnostics V
Country/Territory	United States
City	San Jose, CA
Period	1/24/09 → 1/26/09

ASJC Scopus subject areas

Applied Mathematics
Computer Science Applications
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Condensed Matter Physics

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10.1117/12.807438

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Denoising during optical coherence tomography of the prostate nerves via bivariate shrinkage using dual-tree complex wavelet transform. / Chitchian, Shahab; Fiddy, Michael; Fried, Nathaniel M.
Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 7161 2009. 716112.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chitchian, S, Fiddy, M & Fried, NM 2009, Denoising during optical coherence tomography of the prostate nerves via bivariate shrinkage using dual-tree complex wavelet transform. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 7161, 716112, Photonic Therapeutics and Diagnostics V, San Jose, CA, United States, 1/24/09. https://doi.org/10.1117/12.807438

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AB - 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%.

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

Abstract

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