通过使用双树复数小波变换的小波收缩对前列腺神经进行光学相干断层扫描时的去噪。

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

作者信息

Chitchian Shahab, Fiddy Michael A, Fried Nathaniel M

机构信息

University of North Carolina at Charlotte, Department of Physics and Optical Science, Charlotte, North Carolina 28223, USA.

出版信息

J Biomed Opt. 2009 Jan-Feb;14(1):014031. doi: 10.1117/1.3081543.

DOI:10.1117/1.3081543

PMID:19256719

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.

摘要

双树复数小波变换（CDWT）是离散小波变换（DWT）相对较新的改进，具有重要的附加特性。它在二维及更高维度上几乎是平移不变的且具有方向选择性。在这封信中，一种局部自适应去噪算法被应用于减少前列腺的时域光学相干断层扫描（OCT）图像中的斑点噪声。该算法通过离散小波变换（DWT）和双树复数小波变换（CDWT）进行说明。应用双树复数小波变换（CDWT）在减少OCT图像中的斑点噪声方面提供了更好的结果。海绵体神经和前列腺可以从由噪声引起的不连续处分离出来，并且实现了图像质量指标的改善，信噪比提高了14分贝。