首页 > 论文 > 光学技术 > 45卷 > 1期(pp:102-106)

基于分数阶积分算法的OCT图像去噪研究

Research on OCT image denoising based on fractional integral algorithm

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

散斑存在于光学相干层析成像(OCT)信号中,不可避免地影响OCT图像质量。通过对OCT系统中的噪声源进行分析,提出了一种傅里叶域OCT图像散斑噪声降噪的分数阶积分算法。为了克服单纯主观视觉判别图像质量的局限性,均方误差、峰值信号噪声比和边缘保护系数被选为图像去噪评估标准。通过实验与中值滤波和维纳滤波方法进行比较,结果表明,该算法可以有效地保留OCT图像中的重要边缘细节信息,同时有效消除噪声,使图像细节清晰,提高图像质量。

Abstract

Speckle resides in OCT (optical coherence tomography) signals and inevitably affects OCT image quality. Based on the analysis of noise sources in OCT system, a fractional integration algorithm for speckle noise reduction in Fourier domain OCT images is proposed. In order to overcome the limitation of image quality by subjective visualization, MSE (mean square error),PSNR(peak signal to noise ratio) and EPI (edge preservation index) are chosen as the image denoising evaluation criterions. The method is compared with the Median filter and Wiener filter. The results demonstrate that the algorithm can effectively preserve the important edge detail information of the OCT image while removing the noise, details of the image is clearer and the image quality is well improved.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP312

所属栏目:生物光学

基金项目:国家自然科学基金青年科学基金(6130115); 上海市自然科学基金(13ZR1457900); 上海市科委产学研医项目(15DZ1940400)

收稿日期:2018-02-03

修改稿日期:2018-03-19

网络出版日期:--

作者单位    点击查看

秦显富:上海理工大学 教育部现代微创医疗器械及技术工程研究中心 生物医学光学与视光学研究所, 上海 200093
陈明惠:上海理工大学 教育部现代微创医疗器械及技术工程研究中心 生物医学光学与视光学研究所, 上海 200093
贾文宇:上海理工大学 教育部现代微创医疗器械及技术工程研究中心 生物医学光学与视光学研究所, 上海 200093
何锦涛:上海理工大学 教育部现代微创医疗器械及技术工程研究中心 生物医学光学与视光学研究所, 上海 200093
郑刚:上海理工大学 教育部现代微创医疗器械及技术工程研究中心 生物医学光学与视光学研究所, 上海 200093

联系人作者:秦显富(18917077919@163.com)

备注:秦显富(1993-),男,硕士研究生,从事生物医学光子学研究。

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

【2】Fercher A F, Drexler W, Hitzenberger C K, et al. Optical coherence tomography-principles and applications[J]. Rep. Prog. Phys,2003,66(2):239-303.

【3】Zysk A M, Nguyen F T, Oldenburg A L, et al. Optical coherence tomography: a review of clinical development from bench to bedside[J]. Journal of Biomedical Optics,2007,12(5):051403.

【4】Goodman J W. Some fundamental properties of speckle[J]. Opt. Soc. Am,1976,66(11):1145-1150.

【5】Liu X, Ramella-Roman J C, Huang Y, et al. Robust spectral-domain optical coherence tomography speckle model and its cross-correlation coefficient analysis[J]. Opt. Soc. Am,2013,30(1):51-59.

【6】Xu M J, Yang J Z, Zhao D Z. An image-enhancement method based on variable-order fractional differential operators[J]. Bio-Medical Materials and Engineering,2015,26(Suppl1):1325-1333.

【7】Boghaie A, D’Souza R M, Yu Z. Sparse and low rank decomposition based batch image alignment for speckle reduction of retinal OCT images[C]∥IEEE International Symposium on Biomedical Imaging, IEEE,2015

【8】Damber Thapaa, Kaamran Raahemifarc, Vasudevan Lakshminarayananabd. 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.

【9】Jinming D, Wenqi L, Christopher, et al. Denoising optical coherence tomography using second order total generalized variation decomposition[J]. Biomedical Signal Processing and Control,2016,24(11):120-127.

【10】Sudeepa P V, Issac Niwasbc S, Palanisamya P, et al. Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering[J]. Comput Biol Med,2016,71(C):97-107.

【11】Kim J, Miller D, Kim E, et al. Optical coherence tomography speckle reduction by a partially spatially coherent source[J]. Journal of Biomedical Optics,2005,10(6):1-9.

【12】Desjardins A E, Vakoc B J, Motaghiannezam S M, et al. Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction[J]. Opt. Express,2007,15(10):6200-6209.

【13】Qiang Chen, Luis de Sisternes, Theodore Leng, et al. Application of improved homogeneity similarity-based denoising in optical coherence tomography retinal images[J]. Journal of Digital Imaging,2015,28(3):346-361.

【14】Avanaki M R N, Laissue P P, Eom T J, et al. Speckle reduction using an artificial neural network algorithm[J]. Appl. Opt.,2013,52(21):5050-5057.

【15】Hyoung Park1, HyunSeoKang1. Enhancedoptical coherence tomography imaging using a histogram-based denoising algorithm[J].Optical Engineering,2015,54(11):1-4.

【16】Bernardes R, Maduro C, Serranho P, et al. Improved adaptive complex diffusion despeckling filter[J]. Opt. Express,2010,18(23),24048-24059.

【17】Aja F, Alberola L. On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering[J]. IEEE Trans. Image Process,2006,15(9): 2694-2701.

【18】Zhang X. A denoising approach via wavelet domain diffusion and image domain diffusion[J]. Multimedia Tools & Applications,2016,76(11):1-17.

【19】Baghaie A, D''Souza R M, Yu Z. Application of independent component analysis techniques in speckle noise reduction of retinal OCT images[J]. Optik - International Journal for Light and Electron Optics,2016,127(15):5783-5791.

【20】Kai Yu, Liang Ji, Lei Wang, et al. How to optimize OCT image[J]. Optics Express,2001,9(1):24-35.

【21】Daiqiang Yin, Ying Gu, Ping Xue. Speckle-constrained variational methods for image restoration in optical coherence tomography[J]. Journal of the Optical Society of America, A. Optics, image science, and vision.2013,30(5):456-481.

【22】Farzana Zaki, Yahui Wang, Hao Su, et al. Noise adaptive wavelet thresholding for speckle noise removal in optical coherence tomography[J]. Biomedical Optics Express,2017,8(5):2720-2731.

【23】Anqi Zhang, Jiefeng Xi, Jitao Sun, et al. Pixel-based speckle adjustment for noise reduction in Fourier-domain OCT images[J]. Biomedical Optics Express,2017,8(3):1721-1730.

【24】汪蓉,王笑梅,陈雄,等.基于贝叶斯最小均方差的OCT图像散斑处理[J].上海师范大学学报:自然科学版,2013,42(3):265-270.
Wang Rong, Wang Xiaomei, Chen Xiong, et al. Speckle processing for OCT image based on Bayesian least mean square error criterion[J]. Journal of Shanghai Normal University:Natural Sciences,2013,42(3):265-270.

【25】Popescu D P, Hewko M D, Sowa M G. Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample[J]. Optics Communications,2007,269(1):247-251.

【26】刘新文,王惠楠,钱志余.小波变换对OCT图像的降噪处理[J].光子学报,2006,35(6):935-939.
Liu Xinwen, Wang Huinan, Qian Zhiyu. Denoising process of OCT image based on wavelet transform[J]. Acta Photonica Sinica,2006,35( 6):935-939.

【27】李佳,王笑梅.OCT图像降噪混合滤波方法[J].计算机工程与设计,2011,32(5):1738-1741.
Li Jia, Wang Xiaomei. Mixed filtering method for OCT image denoising[J].Computer Engineering and Design,2011,32(5):1738-1741.

【28】李济舟,杨余飞,王立科,等.一种OCT图像去噪的原始-对偶算法[J].集成技术,2012,1(02):61-64.
Li Jizhou, Yang Yufei, Wang Like, et al. A primal-dual algorithm for OCT image denoising[J]. Journal of Integration Technology,2012,1(02):61-64.

【29】黄果, 蒲亦非, 陈庆利, 等. 基于分数阶积分的图像去噪[J].系统工程与电子技术,2011,33(04):925-932.
Huang Guo, Pu Yifei, Chen Qingli, et al. Research on image denoising based on fractional order integral[J]. Systems Engineering and Electronics, 2011,33(04):925-932.

引用该论文

QIN Xianfu,CHEN Minghui,JIA Wenyu,HE Jintao,ZHENG Gang. Research on OCT image denoising based on fractional integral algorithm[J]. Optical Technique, 2019, 45(1): 102-106

秦显富,陈明惠,贾文宇,何锦涛,郑刚. 基于分数阶积分算法的OCT图像去噪研究[J]. 光学技术, 2019, 45(1): 102-106

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF