Segmentation of layers in retinal images obtained by optical coherence tomography (OCT) has become an important clinical tool to diagnose ophthalmic diseases. However, due to the susceptibility to speckle noise and shadow of blood vessels etc., the layer segmentation technology based on a single image still fail to reach a satisfactory level. We propose a combination method of structure interpolation and lateral mean filtering (SI-LMF) to improve the signal-to-noise ratio based on one retinal image. Before performing one-dimensional lateral mean filtering to remove noise, structure interpolation was operated to eliminate thickness fluctuations. Then, we used boundary growth method to identify boundaries. Compared with existing segmentations, the method proposed in this paper requires less data and avoids the influence of microsaccade. The automatic segmentation method was verified on the spectral domain OCT volume images obtained from four normal objects, which successfully identified the boundaries of 10 physiological layers, consistent with the results based on the manual determination.

中文翻译：

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结构插值与横向均值滤波相结合的光学相干断层扫描图像视网膜层自动分割

通过光学相干断层扫描（OCT）获得的视网膜图像中的层分割已成为诊断眼科疾病的重要临床工具。然而，由于对散斑噪声、血管阴影等的敏感性，基于单幅图​​像的层分割技术仍然未能达到令人满意的水平。我们提出了一种结构插值和横向均值滤波（SI-LMF）的组合方法，以提高基于一个视网膜图像的信噪比。在进行一维横向均值滤波以去除噪声之前，进行结构插值以消除厚度波动。然后，我们使用边界增长方法来识别边界。与现有的分割相比，本文提出的方法需要较少的数据，并且避免了微眼跳的影响。