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去除人眼视神经头光学相干断层扫描图像中的阴影并增强对比度。

Shadow removal and contrast enhancement in optical coherence tomography images of the human optic nerve head.

机构信息

Department of Bioengineering, Imperial College, London, United Kingdom.

出版信息

Invest Ophthalmol Vis Sci. 2011 Sep 29;52(10):7738-48. doi: 10.1167/iovs.10-6925.

Abstract

PURPOSE

To improve the quality of optical coherence tomography (OCT) images of the optic nerve head (ONH).

METHODS

Two algorithms were developed, one to compensate for light attenuation and the other to enhance contrast in OCT images. The former was borrowed from developments in ultrasound imaging and was proven suitable with either time- or spectral-domain OCT. The latter was based on direct application of pixel intensity exponentiation. The performances of these two algorithms were tested on spectral-domain OCT images of four adult ONHs.

RESULTS

Application of the compensation algorithm significantly reduced the intralayer contrast (from 0.74 ± 0.16 to 0.17 ± 0.12; P < 0.001), indicating successful blood vessel shadow removal. Furthermore, compensation dramatically improved the visibility of deeper ONH tissues, such as the peripapillary sclera and lamina cribrosa. Application of the contrast-enhancement algorithm significantly increased the interlayer contrast (from 0.48 ± 0.22 to a maximum of 0.89 ± 0.05; P < 0.001) and thus allowed a better differentiation of tissue boundaries. Contrast enhancement was robust only when compensation was considered.

CONCLUSIONS

The proposed algorithms are simple and can significantly improve the quality of ONH images clinically captured with OCT. This study has important implications, as it will help improve our ability to perform automated segmentation of the ONH; quantify the morphometry and biomechanics of ONH tissues in vivo; and identify potential risk indicators for glaucoma.

摘要

目的

提高视神经头(ONH)光学相干断层扫描(OCT)图像的质量。

方法

开发了两种算法,一种用于补偿光衰减,另一种用于增强 OCT 图像的对比度。前者借鉴了超声成像的发展,并被证明适用于时间域或光谱域 OCT。后者基于像素强度指数的直接应用。在四个成人 ONH 的光谱域 OCT 图像上测试了这两种算法的性能。

结果

补偿算法的应用显著降低了层内对比度(从 0.74 ± 0.16 降至 0.17 ± 0.12;P < 0.001),表明血管阴影去除成功。此外,补偿极大地提高了视神经头深层组织(如视盘周围巩膜和筛板)的可见度。对比度增强算法的应用显著增加了层间对比度(从 0.48 ± 0.22 增加到最大 0.89 ± 0.05;P < 0.001),从而更好地区分了组织边界。只有在考虑补偿的情况下,对比度增强才是稳健的。

结论

所提出的算法简单,可以显著提高临床采集的 ONH OCT 图像的质量。这项研究具有重要意义,因为它将有助于提高我们自动分割 ONH 的能力;量化活体 ONH 组织的形态和生物力学;并识别青光眼的潜在风险指标。

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