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Detection and Grading of Hypertensive Retinopathy Using Vessels Tortuosity and Arteriovenous Ratio

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Abstract

Hypertensive retinopathy (HR) refers to changes in the morphological diameter of the retinal vessels due to persistent high blood pressure. Early detection of such changes helps in preventing blindness or even death due to stroke. These changes can be quantified by computing the arteriovenous ratio and the tortuosity severity in the retinal vasculature. This paper presents a decision support system for detecting and grading HR using morphometric analysis of retinal vasculature, particularly measuring the arteriovenous ratio (AVR) and retinal vessel tortuosity. In the first step, the retinal blood vessels are segmented and classified as arteries and veins. Then, the width of arteries and veins is measured within the region of interest around the optic disk. Next, a new iterative method is proposed to compute the AVR from the caliber measurements of arteries and veins using Parr–Hubbard and Knudtson methods. Moreover, the retinal vessel tortuosity severity index is computed for each image using 14 tortuosity severity metrics. In the end, a hybrid decision support system is proposed for the detection and grading of HR using AVR and tortuosity severity index. Furthermore, we present a new publicly available retinal vessel morphometry (RVM) dataset to evaluate the proposed methodology. The RVM dataset contains 504 retinal images with pixel-level annotations for vessel segmentation, artery/vein classification, and optic disk localization. The image-level labels for vessel tortuosity index and HR grade are also available. The proposed methods of iterative AVR measurement, tortuosity index, and HR grading are evaluated using the new RVM dataset. The results indicate that the proposed method gives superior performance than existing methods. The presented methodology is a novel advancement in automated detection and grading of HR, which can potentially be used as a clinical decision support system.

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Data Availability

The annotated dataset is available at http://vision.seecs.edu.pk/datasets. This work is part of the first author PhD thesis defended on 27 August 2020; the thesis and the related source code are protected by copyrights law No. 404-2021 in Ministry of Economics in UAE and 153 counties.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Sufian A. Badawi, Muhammad Moazam Fraz, Muhammad Shehzad & Imran Mahmood

  2. Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE

    Sajid Javed

  3. Department of Ophthalmology, RAK Medical and Health Sciences University, Ras Al Khaimah, UAE

    Emad Mosalam & Ajay Kamath Nileshwar

  4. Department of Ophthalmology, Saqr Hospital, Ministry of Health and Prevention, Ras Al Khaimah, UAE

    Emad Mosalam & Ajay Kamath Nileshwar

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  1. Sufian A. Badawi
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  2. Muhammad Moazam Fraz
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Correspondence to Muhammad Moazam Fraz.

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Badawi, S.A., Fraz, M.M., Shehzad, M. et al. Detection and Grading of Hypertensive Retinopathy Using Vessels Tortuosity and Arteriovenous Ratio. J Digit Imaging 35, 281–301 (2022). https://doi.org/10.1007/s10278-021-00545-z

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