title: Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform.
abstract: The dual-tree complex wavelet transform (CDWT) is a relatively recent enhancement to the discrete wavelet transform (DWT), with important additional properties. It is nearly shift-invariant and directionally selective in two and higher dimensions. In this letter, a locally adaptive denoising algorithm is applied to reduce speckle noise in time-domain optical coherence tomography (OCT) images of the prostate. The algorithm is illustrated using DWT and CDWT. Applying the CDWT provides improved results for speckle noise reduction in OCT images. The cavernous nerve and prostate gland can be separated from discontinuities due to noise, and image quality metrics improvements with a signal-to-noise ratio increase of 14 dB are attained.
authors.affiliation: University of North Carolina at Charlotte, Department of Physics and Optical Science, Charlotte, North Carolina 28223, USA. schitchi@uncc.edu
authors.forename: Shahab
authors.initials: S
authors.lastname: Chitchian
authors.forename: Michael A
authors.initials: MA
authors.lastname: Fiddy
authors.forename: Nathaniel M
authors.initials: NM
authors.lastname: Fried
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010525:Peripheral Nerves / Q000166:cytology*; D011467:Prostate / Q000166:cytology* / Q000294:innervation*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence / Q000379:methods*