IEEE Trans Med Imaging. 2018 Nov;37(11):2536-2546. doi: 10.1109/TMI.2018.2838550. Epub 2018 May 21.
Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration, and diabetic retinopathy. With the development of computer science, computer aided diagnosis has been developed to process and analyze the retinal images automatically. One of the challenges in the analysis is that the quality of the retinal image is often degraded. For example, a cataract in human lens will attenuate the retinal image, just as a cloudy camera lens which reduces the quality of a photograph. It often obscures the details in the retinal images and posts challenges in retinal image processing and analyzing tasks. In this paper, we approximate the degradation of the retinal images as a combination of human-lens attenuation and scattering. A novel structure-preserving guided retinal image filtering (SGRIF) is then proposed to restore images based on the attenuation and scattering model. The proposed SGRIF consists of a step of global structure transferring and a step of global edge-preserving smoothing. Our results show that the proposed SGRIF method is able to improve the contrast of retinal images, measured by histogram flatness measure, histogram spread, and variability of local luminosity. In addition, we further explored the benefits of SGRIF for subsequent retinal image processing and analyzing tasks. In the two applications of deep learning-based optic cup segmentation and sparse learning-based cup-to-disk ratio (CDR) computation, our results show that we are able to achieve more accurate optic cup segmentation and CDR measurements from images processed by SGRIF.
眼底照片已被用于许多眼部疾病的诊断，如青光眼、病理性近视、年龄相关性黄斑变性和糖尿病性视网膜病变。随着计算机科学的发展，已经开发出计算机辅助诊断来自动处理和分析视网膜图像。分析中的一个挑战是视网膜图像的质量通常会下降。例如，人眼晶状体中的白内障会使视网膜图像衰减，就像镜头模糊的相机镜头会降低照片的质量一样。它经常使视网膜图像中的细节变得模糊不清，并对视网膜图像处理和分析任务提出挑战。在本文中，我们将视网膜图像的退化近似为人眼晶状体衰减和散射的组合。然后提出了一种新的结构保持引导的视网膜图像滤波（SGRIF）方法，该方法基于衰减和散射模型来恢复图像。所提出的 SGRIF 由全局结构传递步骤和全局边缘保持平滑步骤组成。我们的结果表明，所提出的 SGRIF 方法能够通过直方图平坦度测量、直方图扩展和局部亮度变化来提高视网膜图像的对比度。此外，我们还进一步探讨了 SGRIF 对后续视网膜图像处理和分析任务的益处。在基于深度学习的视杯分割和基于稀疏学习的杯盘比（CDR）计算的两个应用中，我们的结果表明，我们能够从经过 SGRIF 处理的图像中实现更准确的视杯分割和 CDR 测量。
