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光热光学相干层析成像中基于小波变换的旋转核变换去噪算法

Rotating Kernel Transformation Denoising Algorithm Based on Wavelet Transform in Photothermal Optical Coherence Tomography

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摘要

针对光热光学相干层析(PT-OCT)三维图像中有不同类型的散斑噪声,采用改进的旋转核算法对其进行抑制。首先对PT-OCT图像进行小波分解,获得4个不同频带的子图像;然后利用最大类间方差算法分离低频近似子图像的前景和背景,并对其进行分段增强,使用改进的RKT算法分别对水平、垂直和对角三个方向的高频细节图像进行滤波;最后对增强处理后的低频近似图像与三个旋转核滤波后的高频细节图像进行线性增强,再对其进行重构,得到去噪后的图像。所提算法对于大脑等复杂组织的血管造影截面图像和在不同深度的切片层析图像,能够有效降低PT-OCT图像血管间的散斑噪声,比经典的RKT算法的方均根误差平均降低27.16,平均峰值信噪比提高3.68dB,从而提高血管造影的质量。

Abstract

In view of the different types of speckle noise in the photothermal optical coherence tomography (PT-OCT) three-dimensional image, an improved rotating kernel algorithm is used to suppress them. First, the PT-OCT images are decomposed by wavelet, and four sub-images with different frequency bands are obtained. Then, the foreground and background of the low-frequency approximation sub-images are separated by the maximum between-class variance algorithm, and the segmented enhancement is performed. The improved RKT algorithm is used to filter the high frequency detailed images in horizontal, vertical and diagonal directions respectively. Finally, the low frequency approximate image and the high frequency detail image after three rotating core filtering are linearly enhanced, and then reconstructed to obtain the de-noised image. The proposed algorithm can effectively reduce the speckle noise between vessels in PT-OCT images for angiographic cross section images of brain and other complex tissues and section tomography images at different depths. Compared with the classical RKT algorithm, the square-root mean error is reduced by 27.16 on average, and the average peak signal-to-noise ratio is increased by 3.68dB, which can improve the quality of angiography imaging.

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中图分类号:TP751

DOI:10.3788/LOP57.221005

所属栏目:图像处理

基金项目:国家自然科学基金;

收稿日期:2020-02-03

修改稿日期:2020-03-30

网络出版日期:2020-11-01

作者单位    点击查看

黄伟源:华南师范大学物理与电信工程学院, 广东 广州 510006
吴家怡:华南师范大学物理与电信工程学院, 广东 广州 510006
任汉宏:华南师范大学物理与电信工程学院, 广东 广州 510006
吴南寿:华南师范大学物理与电信工程学院, 广东 广州 510006
魏波:华南师范大学物理与电信工程学院, 广东 广州 510006
唐志列:华南师范大学物理与电信工程学院, 广东 广州 510006华南师范大学国家级物理学科基础课实验教学示范中心, 广东 广州 510006

联系人作者:唐志列(tangzhl@scun.cdu.com)

备注:国家自然科学基金;

【1】Huang D, Swanson E, Lin C, et al. Optical coherence tomography [J]. Science. 1991, 254(5035): 1178-1181.

【2】Xu M J, Yang J Z, Zhao D Z, et al. An image-enhancement method based on variable-order fractional differential operators [J]. Bio-Medical Materials and Engineering. 2015, 26(S1): S1325-S1333.

【3】Rogowska J, Brezinski M E. Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images [J]. Physics in Medicine and Biology. 2002, 47(4): 641-655.

【4】Chen Y, Li Z L, Nan N, et al. Wavelength misalignment analysis and spectral calibration for Fourier domain polarization-sensitive optical coherence tomography [J]. Chinese Journal of Lasers. 2018, 45(2): 0207022.
陈艳, 李中梁, 南楠, 等. 偏振频域OCT系统光谱错位分析及光谱校准 [J]. 中国激光. 2018, 45(2): 0207022.

【5】He Q Y, Li Z L, Wang X Z, et al. Automated retinal layer segmentation based on optical coherence tomographic images [J]. Acta Optica Sinica. 2016, 36(10): 1011003.
贺琪欲, 李中梁, 王向朝, 等. 基于光学相干层析成像的视网膜图像自动分层方法 [J]. 光学学报. 2016, 36(10): 1011003.

【6】Yuan Z L, Chen J B, Huang W Y, et al. Speckle noise reduction of optical coherence tomography based on robust principle component analysis algorithm [J]. Acta Optica Sinica. 2018, 38(5): 0511002.
袁治灵, 陈俊波, 黄伟源, 等. 基于稳健性主成分分析算法的光学相干层析成像去除散斑噪声的研究 [J]. 光学学报. 2018, 38(5): 0511002.

【7】Schmitt J M, Xiang S H, Yung K M. Speckle in optical coherence tomography [J]. Journal of Biomedical Optics. 1999, 4(1): 95-105.

【8】Iftimia N, Bouma B E, Tearney G J. Speckle reduction in optical coherence tomography by “path length encoded” angular compounding [J]. Journal of Biomedical Optics. 2003, 8(2): 260-263.

【9】Deng B H, Chen J H, Hu M H, et al. Application and imaging processing algorithm of biospeckle technology in fruit quality detection [J]. Laser & Optoelectronics Progress. 2019, 56(9): 090003.
邓博涵, 陈嘉豪, 胡孟晗, 等. 生物散斑技术在水果品质检测中的应用及图像处理算法进展 [J]. 激光与光电子学进展. 2019, 56(9): 090003.

【10】Thapa D, Raahemifar K, Lakshminarayanan V. Reduction of speckle noise from optical coherence tomography images using multi-frame weighted nuclear norm minimization method [J]. Journal of Modern Optics. 2015, 62(21): 1856-1864.Thapa D, Raahemifar K, Lakshminarayanan V. Reduction of speckle noise from optical coherence tomography images using multi-frame weighted nuclear norm minimization method [J]. Journal of Modern Optics. 2015, 62(21): 1856-1864.

【11】Duan J M, Lu W Q, Tench C, et al. Denoising optical coherence tomography using second order total generalized variation decomposition [J]. Biomedical Signal Processing and Control. 2016, 24: 120-127.

【12】Sudeep P V, Issac Niwas S, Palanisamy P, et al. Enhancement and bias removal of optical coherence tomography images: an iterative approach with adaptive bilateral filtering [J]. Computers in Biology and Medicine. 2016, 71: 97-107.

【13】Skala M C, Crow M J, Wax A, et al. Photothermal optical coherence tomography of epidermal growth factor receptor in live cells using immunotargeted gold nanospheres [J]. Nano Letters. 2008, 8(10): 3461-3467.

【14】Tucker-Schwartz J M, Meyer T A, Patil C A, et al. In vivo imaging of gold nanorod contrast agents using photothermal optical coherence tomography [J]. Proceedings of SPIE. 2013, 8571: 85712C.

【15】Jeon W, Jeong W, Son K, et al. Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks [J]. Optics Letters. 2018, 43(17): 4240-4243.

【16】Cai X O, Ni X J. Study on reduction of speckle noise in reconstructed image of digital hologram [J]. Laser & Optoelectronics Progress. 2013, 50(5): 050901.
蔡晓鸥, 倪小静. 数字全息再现像散斑噪声消除的研究 [J]. 激光与光电子学进展. 2013, 50(5): 050901.

【17】Zhou K C, Qian R, Degan S, et al. Optical coherence refraction tomography [J]. Nature Photonics. 2019, 13(11): 794-802.

【18】Rubinoff I, Beckmann L, Wang Y, et al. Speckle reduction in visible-light optical coherence tomography using scan modulation [J]. Neurophotonics. 2019, 6(4): 041107.

【19】Shi F, Cai N, Gu Y B, et al. DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images [J]. Physics in Medicine and Biology. 2019, 64(17): 175010.

【20】Chen H G, Fu S J, Wang H, et al. Speckle reduction based on fractional-order filtering and boosted singular value shrinkage for optical coherence tomography image [J]. Biomedical Signal Processing and Control. 2019, 52: 281-292.

【21】Tang P J, Liu S J, Chen J B, et al. Cross-correlation photothermal optical coherence tomography with high effective resolution [J]. Optics Letters. 2017, 42(23): 4974-4977.

【22】Mallat S G. A theory for multiresolution signal decomposition: the wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989, 11(7): 674-693.

【23】Zhang C X, Chen M H, Wang F, et al. Optical coherence tomography image denoising algorithm based on wavelet transform and fractional integral [J]. Laser & Optoelectronics Progress. 2019, 56(18): 181008.
张晨曦, 陈明惠, 王帆, 等. 小波变换和分数阶积分结合的OCT图像去噪算法 [J]. 激光与光电子学进展. 2019, 56(18): 181008.

【24】Rogowska J, Brezinski M E. Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging [J]. IEEE Transactions on Medical Imaging. 2000, 19(12): 1261-1266.

【25】Wei W Y. New Ostu image segmentation based on intensity stretching on DWT fields [J]. Journal of Northwest Normal University (Natural Science). 2009, 45(6): 46-48.
魏伟一. 基于小波域灰度拉伸的Ostu图像分割 [J]. 西北师范大学学报(自然科学版). 2009, 45(6): 46-48.

引用该论文

Huang Weiyuan,Wu Jiayi,Ren Hanhong,Wu Nanshou,Wei Bo,Tang Zhilie. Rotating Kernel Transformation Denoising Algorithm Based on Wavelet Transform in Photothermal Optical Coherence Tomography[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221005

黄伟源,吴家怡,任汉宏,吴南寿,魏波,唐志列. 光热光学相干层析成像中基于小波变换的旋转核变换去噪算法[J]. 激光与光电子学进展, 2020, 57(22): 221005

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