基于人体眼底图像的青光眼诊断方法

S. V. Komkova
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引用次数: 0

摘要

研究的目的是开发一种检测青光眼的技术,该技术基于计算光学杯和光学盘的尺寸比以及 "四象限 "规则。它们的使用提高了人类视网膜图像中青光眼检测的准确性。本研究提出了一种青光眼检测技术,该技术以光杯垂直直径与光盘垂直直径之比以及 "四象限 "法则作为检测青光眼的两个主要参数。使用面积扩展法和分水岭法对视神经盘(OD)和眼杯(OCH)进行分割,然后将其合并以获得最终结果。它们的合并使用逻辑运算 OR 进行。生成的图像使用圆形近似法进行近似,因为只需计算一个中心和半径即可简单实现。在诊断方面,决定使用两个参数:杯和盘的比率(OCD)和 "四象限 "规则。结果:对从 4 个数据库中获得的视网膜图像进行了研究:HRF、DIARETDB1、DRIONS-DB 和 Messidor。研究表明,在 84 幅视网膜图像中,所提出的技术能正确识别出 75 幅为青光眼图像,总灵敏度为 91.67%。在 163 张正常图像中,154 张被正确分类为正常,特异性为 94.47%。所提出的方法简单,计算效率高。它可有效地用于青光眼早期阶段的计算机诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method of Diagnosis of Glaucoma Based on Human Fundus Images
The purpose of the research to develop a technique for detecting glaucoma, which is based on calculating the size ratio of the optical cup and optical disc and the "four quadrants" rule. Their use increases the accuracy of glaucoma detection in human retina images.Methods. A glaucoma detection technique is proposed that uses the ratio of the vertical diameter of the cup to the vertical diameter of the disc and the "four quadrants" rule as the two main parameters for the detection of glaucoma. The optic nerve disc (OD), the ocular cup (OCH) are segmented using the area extension method and the watershed method, and then combined to obtain the final results. Their union is performed using the logical operation OR. The resulting images are approximated using circular approximation, since its implementation is simple by calculating a single center and radius. For diagnostics, it was decided to use two parameters: the ratio of the cup and the disc (OCD) and the rule of "four quadrants". Their combined assessment makes it possible to increase the accuracy of glaucoma detection.Results: the study of the proposed technique was performed on retinal images obtained from 4 databases: HRF, DIARETDB1, DRIONS-DB, Messidor. The study showed that the proposed technique correctly identifies 75 retinal images as glaucoma out of 84 with a total sensitivity of 91.67%. Of the 163 normal images, 154 were correctly classified as normal with a specificity of 94.47%.Conclusion. The proposed method is simple and computationally efficient. It can be effectively used in computer diagnostics of glaucoma in the early stages of the disease.
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