基于光学相干层析成像的视网膜图像自动分层方法

贺琪欲; 李中梁; 王向朝; 南楠; 卢宇

doi:doi:10.3788/aos201636.1011003

光学学报, 2016, 36 (10): 1011003, 网络出版: 2016-10-12

基于光学相干层析成像的视网膜图像自动分层方法下载： 748次

Automated Retinal Layer Segmentation Based on Optical Coherence Tomographic Images

论文大纲

贺琪欲 ^1,2,*李中梁 ^1,2王向朝 ^1,2南楠 ¹卢宇 ^1,2

作者单位

¹ 中国科学院上海光学精密机械研究所信息光学与光电技术实验室, 上海 201800

² 中国科学院大学, 北京 100049

成像系统光学相干层析成像视网膜自动分层眼科 imaging systems optical coherence tomography automated retinal layer segmentation ophthalmology

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摘要

利用光学相干层析成像(OCT)获得视网膜图像并对其进行分层，进而获得各视网膜层的厚度，在许多眼科疾病的临床诊断中具有重要作用。高散斑噪声、低图像对比度、存在血管等复杂结构等因素使得对视网膜的精确分层难以实现。提出了一种视网膜OCT图像的自动分层方法，利用三维块匹配和均值滤波去噪对图像进行预处理，分两步对视网膜图像分层，在每个A扫描上设置可变阈值进行逐层分割作为初步分层结果，然后对各层的初步分层结果进行连续性和完整性判断和修正。对健康和患病视网膜的OCT图像进行分层以验证提出方法的有效性。实验结果显示该方法能够精确地分出9层视网膜层，平均层边界位置偏差为(1.34±0.24) pixel。该方法能够适应噪声高、对比度低的图像，对存在血管等复杂结构的图像同样能够实现较好的分层。

Abstract

Segmentation of retinal images obtained by optical coherence tomography (OCT) and retinal thickness measurement has become an important clinical diagnostic tool for many diseases in ophthalmology. However, such factors as speckle noise, low image contrast, and irregularly shaped structural features including blood vessels make it difficult to segment retinal layers accurately. An automated retinal layer segmentation method is proposed by employing block-matching and 3D filtering along with mean filtering for preprocessing and a two-step optimal search. The two-step optimal search begins with individual retinal layer segmentation by setting a variable threshold on each A-scan as initial results, which are then checked and corrected for continuity and integrity. The performance of the proposed method is tested on a set of OCT retinal images acquired from healthy people and patients. The experimental results show that the proposed method provides accurate segmentation of nine retinal layers whose mean boundary position deviation is (1.34±0.24) pixel. The method can be applied to OCT images affected by speckle noise, low image contrast, and even irregularly shaped structural features such as blood vessels.

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贺琪欲, 李中梁, 王向朝, 南楠, 卢宇. 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. He Qiyu, Li Zhongliang, Wang Xiangzhao, Nan Nan, Lu Yu. Automated Retinal Layer Segmentation Based on Optical Coherence Tomographic Images[J]. Acta Optica Sinica, 2016, 36(10): 1011003.

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