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扫频光学相干层析视网膜图像配准去噪算法

蔡怀宇 韩晓艳 娄世良 汪毅 陈文光 陈晓冬

蔡怀宇, 韩晓艳, 娄世良, 汪毅, 陈文光, 陈晓冬. 扫频光学相干层析视网膜图像配准去噪算法[J]. 中国光学, 2021, 14(2): 289-297. doi: 10.37188/CO.2020-0130
引用本文: 蔡怀宇, 韩晓艳, 娄世良, 汪毅, 陈文光, 陈晓冬. 扫频光学相干层析视网膜图像配准去噪算法[J]. 中国光学, 2021, 14(2): 289-297. doi: 10.37188/CO.2020-0130
CAI Huai-yu, HAN Xiao-yan, LOU Shi-liang, WANG Yi, CHEN Wen-guang, CHEN Xiao-dong. Speckle noise reduction in swept-source optical coherence tomography by retinal image registration[J]. Chinese Optics, 2021, 14(2): 289-297. doi: 10.37188/CO.2020-0130
Citation: CAI Huai-yu, HAN Xiao-yan, LOU Shi-liang, WANG Yi, CHEN Wen-guang, CHEN Xiao-dong. Speckle noise reduction in swept-source optical coherence tomography by retinal image registration[J]. Chinese Optics, 2021, 14(2): 289-297. doi: 10.37188/CO.2020-0130

扫频光学相干层析视网膜图像配准去噪算法

doi: 10.37188/CO.2020-0130
基金项目: 国家重点研发计划(No. 2017YFC0109901);天津市自然科学基金项目(No. 15JCQNJC14200)
详细信息
    作者简介:

    蔡怀宇(1965—),女,湖南涟源人,博士,教授,硕士生导师,1991年、2000年于天津大学分别获得硕士、博士学位,主要从事信息光学、光电技术及仪器和图像处理等方面的研究。E-mail:hycai@tju.edu.cn

    韩晓艳(1996—),女,内蒙古乌兰察布人,天津大学精密仪器与光电工程技术学院硕士研究生,2018年于南京理工大学获得学士学位,主要从事光学相干层析成像方面的研究。E-mail:xyhan@tju.edu.cn

  • 中图分类号: TN247

Speckle noise reduction in swept-source optical coherence tomography by retinal image registration

Funds: Supported by National Key R&D Program of China (No. 2017YFC0109901); Natural Science Foundation Project of Tianjin (No. 15JCQNJC14200)
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  • 摘要: 多帧叠加平均处理是去除扫频光学相干层析系统散斑噪声、获得较为清晰结构信息的有效方法,但眼睛的震颤、漂移、微眼跳等生理特性和系统光路特性会使图像之间存在错位,导致叠加效果不佳、结构稳定性差,为此本文提出一种基于灰度分布信息和目标几何信息相结合的配准算法。该方法根据图像平均灰度分布提取包含目标信息的感兴趣区域,通过相位相关算法和基于分段拟合的灰度投影算法的双重作用校正图像的平移变换;通过拟合视网膜上边界作为特征点迭代确定最佳旋转参数,并再次重新估计平移参数,实现图像的刚性配准;最后通过轴向扫描一对一映射法以能量函数为约束条件实现图像的非刚性配准。对活体兔眼进行实验,结果表明,本文算法配准后的叠加图像边界清晰,结构信息增强,信噪比和对比度平均有效提高一倍多。本算法适用于强噪声视网膜B-Scans图像的配准,能满足多种类型OCT系统的叠加成像需要,具有较高的鲁棒性和图像配准精度。
  • 图  1  OCT序列图像错位原因。(a)眼睛轴向运动;(b)眼睛垂轴运动;(c)眼睛旋转运动;(d)多角度光线入射

    Figure  1.  Analysis of reasons for geometric transformation of OCT sequence images. (a) Axial eye movement; (b) vertical eye movement; (c) rotational eye movement; (d) multi-angle light incidence

    图  2  配准算法流程图

    Figure  2.  Flow chart of the registration algorithm

    图  3  预处理过程。(a)原始视网膜B-Scan图像;(b)列平均灰度分布图;(c)ROI图像

    Figure  3.  Pretreatment process. (a) Original B-Scan image of retinal image; (b) column average gray distribution map; (c) ROI image

    图  4  分段拟合处理过程。(a)曲线分割结果(黑色虚线为分割线);(b)原始曲线段(最佳连接点A、黑色直线为包络线);(c)处理后的曲线段

    Figure  4.  Fitting process of curve segments. (a) Results of curve segmentation (the dotted black line is the segmentation line); (b) original curve segment results (optimal connection point A, the straight black line is the envelope); (c) curve segment after processing

    图  5  刚性配准结果。(a)上边界提取过程;(b)平移配准后的叠加图;(c)旋转配准后的叠加图(绿色为参考图像,紫色为目标图像)

    Figure  5.  Results of rigid registration. (a) Upper boundary extraction process; (b) averaging image after translation registration; (c) averaging image after rotation registration. (Green is the reference image, purple is the target image)

    图  6  非刚性配准结果。(a)刚性配准后的叠加图及局部放大图;(b)非刚性配准后的叠加图及局部放大图(绿色为参考图像,紫色为目标图像)

    Figure  6.  Results of non-rigid registration. (a) Average image obtained after rigid registration; (b) average image obtained after non-rigid registration (The red dashed box represents an enlarged partial view; Green is the reference image, and purple is the target image)

    图  7  本算法实验结果图。(a)原始单帧图;(b)配准后的叠加图

    Figure  7.  The experimental results obtained by the algorithm proposed in this paper. (a) Original single frame image; (b) averaging image after registration

    图  8  (a)区域选择及SNR(b)和CNR(c)随叠加帧数递增的曲线变化图

    Figure  8.  (a) Area selection, SNR (b) and CNR (c) curve change with an increasing number of multiple frames

    表  1  本文配准算法较对比算法的提升效果

    Table  1.   Improvement performance of proposed algorithm comparing with other algorithms

    比较对象SNR提高倍数CNR提高倍数
    本文方法/文献[18]方法1.121.19
    本文方法/文献[21]方法1.501.48
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出版历程
  • 收稿日期:  2020-07-23
  • 修回日期:  2020-09-03
  • 网络出版日期:  2021-03-05
  • 刊出日期:  2021-04-01

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