Mishra Akshaya, Wong Alexander, Bizheva Kostadinka, Clausi David A
Dept. of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada.
Opt Express. 2009 Dec 21;17(26):23719-28. doi: 10.1364/OE.17.023719.
Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images.
视网膜层厚度是许多视网膜疾病的重要诊断指标，它通过光学相干断层扫描（OCT）图像作为空间位置的函数进行评估。然而，由于散斑噪声、低图像对比度以及诸如视网膜脱离、黄斑裂孔和玻璃膜疣等形状不规则的形态特征等因素，准确分割各个视网膜层很困难。为了解决这个问题，提出了一种从OCT图像中分割视网膜层的计算机方法。采用了一种高效的基于两步内核的优化方案，首先识别各个层的近似位置，然后对其进行细化以获得各个层的准确分割结果。该算法的性能在一组使用高速、高分辨率OCT系统从健康和患病啮齿动物模型体内获取的视网膜图像上进行了测试。实验结果表明，即使在低图像对比度和OCT图像中存在形状不规则的结构特征等次优条件下，所提出的方法也能为受散斑噪声影响的OCT图像提供准确的分割。
