System Development for Measuring the Arterious Venous Rate (AVR) for the Diagnosis of Hypertensive Retinopathy

Ortiz, Daniel; Cubides, Mauricio; Suárez, Andrés; Zequera, Martha; Quiroga, Julián; Gómez, Jesús María Fernández; Arroyo, Nubia

doi:10.1109/andescon.2012.22

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The segmentation of blood vessel based on Hessian matric has also been done previously, namely by Ortiz et al [5] and Ortiz et al [6]. Both studies combined hessian matric and Gabor filters for segmentation, except that in both studies they did not measure the performance of the segmentation method used, but focused more on determining venous arterial ratios.…”

Section: Introductionmentioning

confidence: 96%

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

Wiharto¹,

Palgunadi²

2019

IJACSA

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Diagnosis of computer-based retinopathic hypertension is done by analyzing of retinal images. The analysis is carried out through various stages, one of which is blood vessel segmentation in retinal images. Vascular segmentation of the retina is a complex problem. This is caused by non-uniform lighting, contrast variations and the presence of abnormalities due to disease. This makes segmentation not successful if it only relies on one method. The aims of this study to segment blood vessels in retinal images. The method used is divided into three stages, namely preprocessing, segmentation and testing. The first stage, preprocessing, is to improve image quality with the CLAHE method and the median filter on the green channel image. The second stage, segmenting using a number of methods, namely, frangi filter, 2D-convolution filtering, median filtering, otsu's thresholding, morphology operation, and background subtraction. The last step is testing the system using the DRIVE and STARE dataset. The test results obtained sensitivity 91.187% performance parameters, 86.896% specificity, and area under the curve (AUC) 89.041%. Referring to the performance produced, the proposed model can be used as an alternative for blood vessel segmentation of retinal images.

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Section: Introductionmentioning

confidence: 96%

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

Wiharto¹,

Palgunadi²

2019

IJACSA

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show abstract

“…Manikis et.al [5], also proposes a framework for diagnosis of hypertensive retinopathy disease with hessian-based segmentation of blood vessels, then calculate the value of the AVR as a sign of the disease. A number of similar studies, ie, performed a diagnosis of hypertensive retinopathy using AVR parameters such as those performed by Ortiz et.al [6], Khitran et.al [7], Triwijoyo et.al [8], Faheem et.al [9] and Muramatsu et.al [10].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Early Hypertensive Retinopathy using Fractal Analysis of Retinal Fundus Image

2018

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Automatic detection of hypertensive retinopathy using improved fuzzy clustering and novel loss function

Bhimavarapu

2023

Multimed Tools Appl

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System Development for Measuring the Arterious Venous Rate (AVR) for the Diagnosis of Hypertensive Retinopathy

Cited by 3 publications

References 8 publications

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

Assessment of Early Hypertensive Retinopathy using Fractal Analysis of Retinal Fundus Image

Automatic detection of hypertensive retinopathy using improved fuzzy clustering and novel loss function

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