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

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