Curvature correction of retinal OCTs using graph-based geometry detection.
Curvature correction of retinal OCTs using graph-based geometry detection.
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DOI：
10.1088/0031-9155/58/9/2925
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发表时间：
2013-05-07
影响因子：
3.5
通讯作者：
Sonka M
中科院分区：
文献类型：
--
作者：
Kafieh R;Rabbani H;Abramoff MD;Sonka M
本文提出了一种新的视网膜光学相干断层扫描（OCT）图像增强和预处理算法。该方法分为两步，第一步是基于圆形对称拉普拉斯模型的小波扩散去噪算法，第二步是基于图的几何检测和基于视网膜超反射复合体（hyperreflective complex， HRC）层的曲率校正。该算法将OCT图像的信噪比从0.89提高到1.49，信噪比（SNR）从18.27 dB提高到30.43 dB。采用该方法对插值曲线进行全自动估计，计算了正常和异常情况下的无符号边界定位误差均值±SD。随机选取200片无病理弯曲切片和50片有病理弯曲切片，误差值分别为2.19±1.25微米和8.53±3.76微米。该算法的一个重要方面是它在强病理图像中检测曲率的能力，超越了以前介绍的方法；与同类方法相比，该方法的速度相对较低。
In this paper, we present a new algorithm as an enhancement and preprocessing step for acquired optical coherence tomography (OCT) images of retina. The proposed method is composed of two steps, first of which is a denoising algorithm with wavelet diffusion based on circular symmetric Laplacian model, and the second part can be described in the terms of graph based geometry detection and curvature correction according to the hyper-reflective complex (HRC) layer in retina. The proposed denoising algorithm showed an improvement of contrast to noise ratio from 0.89 to 1.49 and an increase of signal to noise ratio (OCT image SNR) from 18.27 dB to 30.43 dB. By applying the proposed method for estimation of the interpolated curve using a full automatic method, mean ± SD unsigned border positioning error was calculated for normal and abnormal cases. The error values of 2.19±1.25 micrometers and 8.53±3.76 micrometers were detected for 200 randomly selected slices without pathological curvature and 50 randomly selected slices with pathological curvature, respectively. The important aspect of this algorithm is its ability in detection of curvature in strongly pathological images that surpasses the previously introduced methods; the method is also fast, compared to relatively low speed of the similar methods.
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4.4
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通讯作者：
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