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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7161
DOIs
StatePublished - 2009
EventPhotonic Therapeutics and Diagnostics V - San Jose, CA, United States
Duration: Jan 24 2009Jan 26 2009

Other

OtherPhotonic Therapeutics and Diagnostics V
CountryUnited States
CitySan Jose, CA
Period1/24/091/26/09

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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    Chitchian, S., Fiddy, M., & Fried, N. M. (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] https://doi.org/10.1117/12.807438