Almazroa Ahmed, Sun Weiwei, Alodhayb Sami, Raahemifar Kaamran, Lakshminarayanan Vasudevan
King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia.
Ophthalmology and Visual Science Department, University of Michigan, Ann Arbor, MI, USA.
Clin Ophthalmol. 2017 Nov 15;11:2017-2029. doi: 10.2147/OPTH.S140061. eCollection 2017.
Segmenting the optic disc (OD) is an important and essential step in creating a frame of reference for diagnosing optic nerve head pathologies such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of optic nerve head abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique was applied. As well an important contribution was to involve the variations in opinions among the ophthalmologists in detecting the disc boundaries and diagnosing the glaucoma. Most of the previous studies were trained and tested based on only one opinion, which can be assumed to be biased for the ophthalmologist. In addition, the accuracy was calculated based on the number of images that coincided with the ophthalmologists' agreed-upon images, and not only on the overlapping images as in previous studies. The ultimate goal of this project is to develop an automated image processing system for glaucoma screening. The disc algorithm is evaluated using a new retinal fundus image dataset called RIGA (retinal images for glaucoma analysis). In the case of low-quality images, a double level set was applied, in which the first level set was considered to be localization for the OD. Five hundred and fifty images are used to test the algorithm accuracy as well as the agreement among the manual markings of six ophthalmologists. The accuracy of the algorithm in marking the optic disc area and centroid was 83.9%, and the best agreement was observed between the results of the algorithm and manual markings in 379 images.
对视盘（OD）进行分割是为青光眼等视神经乳头病变的诊断创建参考框架的重要且关键的一步。因此，可靠的视盘分割技术对于自动筛查视神经乳头异常是必要的。本文的主要贡献在于提出了一种基于在局部视盘图像上应用水平集方法的新型视盘分割算法。为防止血管干扰水平集过程，应用了一种修复技术。此外，一个重要贡献是在检测视盘边界和诊断青光眼时纳入了眼科医生之间的不同意见。之前的大多数研究仅基于一种意见进行训练和测试，而这可能被认为对眼科医生有偏差。此外，准确性是基于与眼科医生达成一致的图像数量来计算的，而不像之前的研究那样仅基于重叠图像。该项目的最终目标是开发一种用于青光眼筛查的自动化图像处理系统。使用一个名为RIGA（用于青光眼分析的视网膜图像）的新视网膜眼底图像数据集对视盘算法进行评估。在低质量图像的情况下，应用了双重水平集，其中第一个水平集被认为是对视盘的定位。使用550张图像来测试算法的准确性以及六位眼科医生手动标记之间的一致性。算法对视盘区域和质心的标记准确率为83.9%，在379张图像中观察到算法结果与手动标记之间的最佳一致性。
