Retinal Blood Vessels Segmentation using ISODATA and High Boost Filter

Jadoon, Zainab; Ahmad, Shaban; Jadoon, Muhammad Ammar Khan; Imtiaz, Aqsa; Muhammad, Nazeer; Mahmood, Zahid

doi:10.1109/icomet48670.2020.9074128

Cited by 8 publications

(1 citation statement)

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“…95.29% and 95.47% accuracies were obtained on DRIVE and STARE databases, respectively. Jadoon et al (2020) proposed a vessel extraction algorithm in which the CLAHE algorithm is applied on the green channel of fundus images, followed by the top-hat operation [29]. Prominent vessels are obtained using Frangi as well as high boost filters.…”

Section: Dash Andmentioning

confidence: 99%

A Novel Threshold based Method for Vessel Intensity Detection and Extraction from Retinal Images

FatinaWahid¹,

Sugandhi²,

Raju³

et al. 2021

IJACSA

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Retinal vessel segmentation is an active research area in medical image processing. Several research outcomes on retinal vessel segmentation have emerged in recent years. Each method has its own pros and cons, either in the vessel detection stage or in its extraction. Based on a detailed empirical investigation, a novel retinal vessel extraction architecture is proposed, which makes use of a couple of existing algorithms. In the proposed algorithm, vessel detection is carried out using a cumulative distribution function-based thresholding scheme. The resultant vessel intensities are extracted based on the hysteresis thresholding scheme. Experiments are carried out with retinal images from DRIVE and STARE databases. The results in terms of Sensitivity, Specificity, and Accuracy are compared with five standard methods. The proposed method outperforms all methods in terms of Sensitivity and Accuracy for the DRIVE data set, whereas for STARE, the performance is comparable with the best method.

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Section: Dash Andmentioning

confidence: 99%