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基于动静脉比的高血压性视网膜病变检测决策支持系统。

Decision support system for detection of hypertensive retinopathy using arteriovenous ratio.

机构信息

Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.

Department of Computer & Software Engineering, College of E&ME, National University of Sciences and Technology, Islamabad, Pakistan.

出版信息

Artif Intell Med. 2018 Aug;90:15-24. doi: 10.1016/j.artmed.2018.06.004. Epub 2018 Jul 2.

Abstract

Hypertensive Retinopathy (HR) caused by hypertension is a retinal disease which may leads to vision loss and blindness. Computer aided diagnostic systems for various diseases are being used in clinics but there is a need to develop an automated system that detects and grades HR disease. In this paper, an automated system is presented that detects and grades HR disease using Arteriovenous Ratio (AVR).The presented system includes three modules i.e. main component extraction, artery/vein (A/V) classification and finally AVR calculation and grading of HR. Proposed system uses vascular map and a set of hybrid features for A/V classification. The evaluation of proposed system is carried out using three datasets. The proposed system shows average accuracies of 95.14% for images of INSPIRE-AVR database, 96.82% for images of VICAVR database and 98.76% for local dataset AVRDB. These results support that the proposed system is trustworthy for clinical use in detection and grading of HR disease. Main contribution of proposed system is that it utilizes complete blood vessel map for A/V classification. These arteries and veins are then used to calculate AVR and grade HR cases based on AVR values. Another contribution of this article is that it presents a new dataset AVRDB for A/V classification and HR detection.

摘要

高血压性视网膜病变(HR)是由高血压引起的视网膜疾病,可能导致视力丧失和失明。各种疾病的计算机辅助诊断系统已在临床上使用,但需要开发一种自动系统来检测和分级 HR 疾病。本文提出了一种使用动静脉比(AVR)自动检测和分级 HR 疾病的系统。该系统包括三个模块,即主要成分提取、动脉/静脉(A/V)分类以及最终的 AVR 计算和 HR 分级。所提出的系统使用血管图和一组混合特征进行 A/V 分类。使用三个数据集对提出的系统进行了评估。该系统在 INSPIRE-AVR 数据库的图像上的平均准确率为 95.14%,在 VICAVR 数据库的图像上的平均准确率为 96.82%,在本地数据集 AVRDB 上的平均准确率为 98.76%。这些结果表明,该系统在 HR 疾病的检测和分级方面具有临床应用的可信度。所提出的系统的主要贡献是它利用完整的血管图进行 A/V 分类。然后,利用这些动脉和静脉来计算 AVR,并根据 AVR 值对 HR 病例进行分级。本文的另一个贡献是提出了一个新的数据集 AVRDB,用于 A/V 分类和 HR 检测。

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