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

Badawi, Sufian A.; Fraz, Muhammad Moazam; Shehzad, Muhammad; Mahmood, Imran; Javed, Sajid; Mosalam, Emad; Nileshwar, Ajay Kamath

doi:10.1007/s10278-021-00545-z

Cited by 23 publications

(15 citation statements)

References 52 publications

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“…Following the procedure in Figure 2 , the proposed method starts by segmenting a binary image that contains the segmented vessels of the retina using the optimized method in [ 16 ], then using iterative thinning to localize the skeleton of the vessels is extracted. The skeleton is fragmented and optimized to vessel fragment [ 13 ], where each vessel fragment connects two intersection/bifurcation or endpoints in the skeleton. Each vessel fragment extracted from the retinal image is considered a curve, and we apply all the fourteen tortuosity mathematical Formulas ( 1 )–( 14 ) listed.…”

Section: Methodsmentioning

confidence: 99%

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Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

et al. 2022

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Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist’s judgment. Moreover, detecting the tortuosity severity of the retinal vessels’, optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model.

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Section: Methodsmentioning

confidence: 99%

“…AVR is used to determine the severity of hypertensive retinopathy in the majority of currently conducted investigations [ 11 ]. Others have added OD detection [ 11 , 12 ], and others added the tortuosity [ 13 ]. However, extra efforts are needed in disease diagnosing research.…”

Section: Introductionmentioning

confidence: 99%

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

et al. 2022

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“…The diameter of the artery gradually narrows, and the proportion of arteries and veins gradually decreases ( Dziedziak et al, 2022 ). Retinal hemorrhage, hard exudation, cotton velvet spots, and other changes occur in the fundus; and optic disc edema may occur in severe cases ( Liu et al, 2021 ; Badawi et al, 2022 ). According to the progression and severity of the disease, HR is divided into four grades: grade I, vasoconstriction and narrowing; grade II, arteriosclerosis; grade III, exudation, cotton velvet spots, hemorrhage, and extensive microvascular changes; and grade IV grade III changes and optic disc edema ( Wong and Mitchell, 2004 ; Tsukikawa and Stacey, 2020 ).…”

Section: Application Of Artificial Intelligence In Retinal Vascular D...mentioning

confidence: 99%

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Liu

et al. 2023

Front. Cell Dev. Biol.

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As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

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“…Another recent work in this direction was based on an optimized BCOSFIRE filter for vessel segmentation [22], FCN deep learning for the segmented retinal vessels classification to arteries and veins [23], the segmentation of ring-cut, and calculating the iterative-AVR as a new method for measuring AVR that was the first in the field to be implemented and published for arteriovenous ratio [6]. Using a publicly available dataset, 'AV classification' and retinal morphometry datasets form the stages in a decision support system for diagnosing eye diseases from retinal images; however, the skeletonization phase was only briefly explained and described.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the demand for human-free automated retinal vascular segmentation has become crucial. Diagnosing retinal diseases such as diabetic retinopathy (DR) and hypertensive retinopathy (HR) is highly dependent on the study and analysis of blood vessel morphology changes [5,6].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index

Badawi,

Takruri,

ElBadawi

et al. 2023

Mathematics

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Retinal vessel segmentation, skeletonization, and the generation of vessel segments are considered significant steps in any automated system for measuring the vessel biomarkers of several disease diagnoses. Most of the current tortuosity quantification methods rely on precise vascular segmentation and skeletonization of the retinal vessels. Additionally, the existence of a reference dataset for accurate vessel segment images is an essential need for implementing deep learning solutions and an automated system for measuring the vessel biomarkers of several disease diagnoses, especially for optimized quantification of vessel tortuosity or accurate measurement of AV-nicking. This study aimed to present an improved method for skeletonizing and extracting the retinal vessel segments from the 504 images in the AV classification dataset. The study utilized the Six Sigma process capability index, sigma level, and yield to measure the vessels’ tortuosity calculation improvement before and after optimizing the extracted vessels. As a result, the study showed that the sigma level for the vessel segment optimization improved from 2.7 to 4.39, the confirming yield improved from 88 percent to 99.77 percent, and the optimized vessel segments of the AV classification dataset retinal images are available in monochrome and colored formats.

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

Cited by 23 publications

References 52 publications

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index

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