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眼科光学相干层析成像的图像处理方法

蔡怀宇 张玮茜 陈晓冬 刘珊珊 韩晓艳

蔡怀宇, 张玮茜, 陈晓冬, 刘珊珊, 韩晓艳. 眼科光学相干层析成像的图像处理方法[J]. 中国光学, 2019, 12(4): 731-740. doi: 10.3788/CO.20191204.0731
引用本文: 蔡怀宇, 张玮茜, 陈晓冬, 刘珊珊, 韩晓艳. 眼科光学相干层析成像的图像处理方法[J]. 中国光学, 2019, 12(4): 731-740. doi: 10.3788/CO.20191204.0731
CAI Huai-yu, ZHANG Wei-qian, CHEN Xiao-dong, LIU Shan-shan, HAN Xiao-yan. Image processing method for ophthalmic optical coherence tomography[J]. Chinese Optics, 2019, 12(4): 731-740. doi: 10.3788/CO.20191204.0731
Citation: CAI Huai-yu, ZHANG Wei-qian, CHEN Xiao-dong, LIU Shan-shan, HAN Xiao-yan. Image processing method for ophthalmic optical coherence tomography[J]. Chinese Optics, 2019, 12(4): 731-740. doi: 10.3788/CO.20191204.0731

眼科光学相干层析成像的图像处理方法

doi: 10.3788/CO.20191204.0731
基金项目: 

国家重点研发计划 2017YFC0109901

天津市自然科学基金项目 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

    张玮茜(1994—),女,山西太原人,天津大学精密仪器与光电子工程学院硕士研究生,2017年于山西大学获得学士学位,主要从事光学相干层析成像方面的研究。E-mail:wqzhang330@tju.edu.cn

  • 中图分类号: Q334

Image processing method for ophthalmic optical coherence tomography

Funds: 

National Key R&D Program of China 2017YFC0109901

Natural Science Foundation Project of Tianjin 15JCQNJC14200

More Information
  • 摘要: 光学相干层析成像(OCT)由于具有微米级高分辨率、非接触式成像以及瞬时性等特点,成为临床医学领域的研究热点,近些年得到迅速的发展,取得诸多进展与突破。本文简述了OCT技术在眼科医学中的各类应用及发展现状,分类讨论了OCT图像在空域和频域中的降噪方法,并重点总结了OCT眼前节和视网膜图像中各层组织的精确定位分层方法。其中深入分析了基于灰度值搜索方法、活动轮廓模型、图论和模式识别等分层方法,并针对现有分层方法的优缺点以及存在的问题展开深入讨论,提出相应的解决方法和优化方案。对眼科相关疾病的临床诊断指标分析评价,根据眼科临床医学需求和OCT图像处理现状,对未来OCT图像处理的发展趋势和发展水平做进一步讨论和分析。
  • 图  1  (a) 眼前节OCT图像;(b)视网膜OCT图像

    Figure  1.  (a)OCT of the anterior segment; (b)retinal OCT image

    图  2  各类视网膜病变的OCT图像(a)健康视网膜黄斑中心凹(b)黄斑裂洞(c)黄斑水肿(d)年龄相关黄斑变性(e)中央浆液脉络膜视网膜病(f)增殖性糖尿病视网膜病变

    Figure  2.  OCT images of various retinal diseases. (a)healthy macular fovea; (b)macular hole; (c) macular edema; (d)age-related macular degeneration; (e)central serous retinopathy; (f)proliferative diabetic retinopathy

    图  3  ZAP算法效果图

    Figure  3.  Image processing with ZAP algorithm

    图  4  算法效果图

    Figure  4.  Results obtained with proposed algorithm

    图  5  不同例健康视网膜RPE和ILM分层结果

    Figure  5.  RPE and ILM stratification results of healthy retinal in different cases

    图  6  Mujat等提出的基于活动轮廓的视网膜分层方法

    Figure  6.  Retinal stratification method based on active contour proposed by Mujat

    图  7  INL层分割互补梯度示意图

    Figure  7.  A diagram of a complementary gradient for dividing INL boundary

    图  8  使用SVM对不同病症视网膜OCT成像的分割结果. (a)健康眼; (b)年龄相关黄斑变性; (c)黄斑分离; (d)青光眼

    Figure  8.  Results of SVM segmentation for retinal OCT imaging of different diseases. (a)Healthy eyes; (b)age-related macular degeneration; (c)macular separation; (d)glaucoma

    表  1  OCT视网膜图像分层方法

    Table  1.   Methods of OCT retinal image segmentation

    来源 方法 分割层数 时间 描述
    Fernández, Fabritius等[14, 34] 基于最大灰度的搜索算法 2层 17~21 s 通过定位RPE和ILM测量视网膜厚度,视网膜疾病检测具有局限性
    Mujat,Ghorbel等[33-38] 活动轮廓模型 9层 5 s 局部收敛,分割结果高度依赖曲线的参数选择,且分割范围仅在初始点附近
    Yang等[39-41] 图论 9层 16 s 全局搜索,分层灵活,对噪声和图像退化十分敏感
    Fuller等[42-44] 模式识别 9层 10 min 受噪声影响较小,多用于三维分割,但耗时较长
    下载: 导出CSV

    表  2  OCT临床诊断指标与算法效果对比

    Table  2.   Clinical diagnostic index of OCT and comparison of image processing effect with different algorithms

    病灶名称 临床指标 检测方法 检测误差
    脉络膜视网膜病 中央凹或视网膜色素上皮细胞隆起、内界膜变形 ILM层定位及厚度分割[17] 误差<5 pixels
    年龄相关黄斑变性 视网膜色素上皮细胞脱落、脉络膜毛细血管断裂、色素上皮下脉络膜毛细血管光带增强、光感受器厚度增大 灰度值搜索法测量RPE层厚度[17] 误差>10 pixels
    图论法对RPE层厚度测量[46] 测量误差(1.430±0.20) μm
    通过模式识别对PRL层分割[47] 误差<6 pixels
    青光眼 视神经纤维层厚度变化;
    杯盘比CDR>0.5
    NFL层厚度测量[47]
    视神经乳头杯盘比测量[50]
    误差<6 pixels
    测量误差(7.27±5.4) μm
    下载: 导出CSV
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  • 收稿日期:  2018-11-01
  • 修回日期:  2018-12-28
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