Almazroa Ahmed, Alodhayb Sami, Raahemifar Kaamran, Lakshminarayanan Vasudevan
Kellogg Eye Center, University of Michigan, 1000 Wall St, Ann Arbor, MI 48105, USA.
King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, National Guard, Riyadh 14611, Saudi Arabia.
Int J Biomed Imaging. 2017;2017:4826385. doi: 10.1155/2017/4826385. Epub 2017 Aug 29.
Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma's population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while thresholding using Type-II fuzzy approach was developed for segmenting the cup. The results from the algorithms were verified using the manual markings of images from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by six ophthalmologists. The algorithm's accuracy for HCDR and VCDR combined was 74.2%. Only the accuracy of manual markings by one ophthalmologist was higher than the algorithm's accuracy. The algorithm's best agreement was with markings by ophthalmologist number 1 in 230 images (41.8%) of the total tested images.
水平杯盘比和垂直杯盘比是临床上用于检测青光眼或监测其进展的最关键参数,需从视神经乳头的视网膜眼底图像中手动评估。由于青光眼专家稀缺以及青光眼患者数量不断增加,自动计算水平杯盘比和垂直杯盘比(分别为HCDR和VCDR)对于青光眼筛查可能会有所帮助。我们报告了两种计算HCDR和VCDR的算法。在这些算法中,开发了水平集和修复技术来分割视盘,同时开发了使用II型模糊方法的阈值处理来分割视杯。通过六名眼科医生对青光眼图像数据集(用于青光眼分析的视网膜眼底图像(RIGA数据集))中的图像进行手动标记,对算法结果进行了验证。该算法HCDR和VCDR综合准确率为74.2%。只有一位眼科医生的手动标记准确率高于算法准确率。在总共测试的图像中,该算法与眼科医生1在230张图像(41.8%)中的标记一致性最佳。