Blood Vessel Diameter Measurement on Retinal Image

Abbadi, Nidhal K. El; Saadi, Enas Hamood Al

doi:10.3844/jcssp.2014.879.883

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…We can notice that these diseases have a direct consequence on the regional anatomy of the human retinal blood vessel. In this research study, we are interested in developing an automatic diagnosis system of the changes in levels of the human retina topology that can help the ophthalmologist to diagnose the patient [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

“…The potential pixels were drawn from the edge image using a continuous increment from lower to higher distance and orientation. El Abbadi et al [ 13 ] have suggested a strategy to identify the retinal blood vessel diameter based on mask creation and specifically to measure the blood vessel diameter. Gao et al [ 14 , 15 ] have proposed another method to measure the blood vessel diameter, which incorporates the use of a tracking technique.…”

Section: Introductionmentioning

confidence: 99%

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Novel Computerized Method for Measurement of Retinal Vessel Diameters

et al. 2017

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Several clinical studies reveal the relationship between alterations in the topologies of the human retinal blood vessel, the outcrop and the disease evolution, such as diabetic retinopathy, hypertensive retinopathy, and macular degeneration. Indeed, the detection of these vascular changes always has gaps. In addition, the manual steps are slow, which may be subjected to a bias of the perceiver. However, we can overcome these troubles using computer algorithms that are quicker and more accurate. This paper presents and investigates a novel method for measuring the blood vessel diameter in the retinal image. The proposed method is based on a thresholding segmentation and thinning step, followed by the characteristic point determination step by the Douglas-Peucker algorithm. Thereafter, it uses the active contours to detect vessel contour. Finally, Heron’s Formula is applied to assure the calculation of vessel diameter. The obtained results for six sample images showed that the proposed method generated less errors compared to other techniques, which confirms the high performance of the proposed method.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Computerized Method for Measurement of Retinal Vessel Diameters

et al. 2017

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“…Researchers are increasingly focusing on automating the fundus image analysis step of the ophthalmology diagnostic process. The early detection of these disorders is crucial for ophthalmologists because some of them have the potential to cause blindness in patients 1 . Medical imaging today plays a significant role in many areas of medicine; it makes it possible and simple to acquire, transmit, and analyze medical images while also aiding in medical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An Automated Wavelet Scattering Network Classification Using Three Stages of Cataract Disease

Al-Saadi,

Khdiar,

Al-Saadi

2024

Baghdad Sci.J

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A cataract is an eye disease that causes visual distortion and the late stage of this disease can lead to blindness. It is considered a silent disease that can occur without the appearance of symptoms. Therefore, the most effective way to detect cataracts is through accurate and timely detection to prevent hurting, expensive operations, and to stop blindness. The purpose of this paper is to propose an automated system based on the wavelet scattering network which categorizes the patients into four classes: early cataract, intermediate cataract, late cataract, and non-cataract conditions using 512 images of the ODIR dataset (212 Cataract and 300 of Normal). The first step in this technique is the preprocessing step for the retinal image was a mean filter, which was utilized to reduce the image's noise. The limited contrast adaptive histogram equalization (CLAHE) method was then employed to improve the image's contract level. Then, Low-variance characteristics can be extracted from image data using a wavelet scattering network for use in deep learning applications. In this network, lowpass scaling filters and predefined wavelets are employed. The average accuracy of the suggested method was 100% for four classes (Normal, Early, Moderate, and Severe). The results are promising compared with other similar works.

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“…However, human retina is not resistive to certain diseases including diabetic retinopathy (DR), hypertensive retinopathy, glaucoma, high blood pressure, autoimmune deficiency syndrome, arteriosclerosis, and cardiovascular disease [2]. DR being prevalent among these diseases has been shown to cause measurable changes in the retinal blood vessels' diameter [5], branching angles, width, tortuosity, and length [6]. is has been revealed to have an adverse effect on the identification process and the eventual accuracy of the retinal biometric system [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…e presence of DR could also lead to changes in blood vessel structure and vessel distribution which in turn could result in new vessel growth [13]. Also, changes in the blood vessel diameter are direct indicators of retinal vasculature abnormality traceable to DR [5]. Furthermore, the retina infected by DR shows signs of lesions such as microaneurysms, cotton wool spots, exudates, macular edema, and hemorrhages [14].…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Framework for Healthy and Diabetic Retinopathy Retinal Image Recognition

et al. 2020

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The feature extraction stage remains a major component of every biometric recognition system. In most instances, the eventual accuracy of a recognition system is dependent on the features extracted from the biometric trait and the feature extraction technique adopted. The widely adopted technique employs features extracted from healthy retinal images in training retina recognition system. However, literature has shown that certain eye diseases such as diabetic retinopathy (DR), hypertensive retinopathy, glaucoma, and cataract could alter the recognition accuracy of the retina recognition system. This connotes that a robust retina recognition system should be designed to accommodate healthy and diseased retinal images. A framework with two different approaches for retina image recognition is presented in this study. The first approach employed structural features for healthy retinal image recognition while the second employed vascular and lesion-based features for DR retinal image recognition. Any input retinal image was first examined for the presence of DR symptoms before the appropriate feature extraction technique was adopted. Recognition rates of 100% and 97.23% were achieved for the healthy and DR retinal images, respectively, and a false acceptance rate of 0.0444 and a false rejection rate of 0.0133 were also achieved.

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Blood Vessel Diameter Measurement on Retinal Image

Cited by 9 publications

References 7 publications

Novel Computerized Method for Measurement of Retinal Vessel Diameters

Novel Computerized Method for Measurement of Retinal Vessel Diameters

An Automated Wavelet Scattering Network Classification Using Three Stages of Cataract Disease

Implementation of a Framework for Healthy and Diabetic Retinopathy Retinal Image Recognition

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