Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve a...

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Veröffentlicht in:Electronics (Basel) 2021-09, Vol.10 (18), p.2297
Hauptverfasser: Soomro, Toufique A., Ali, Ahmed, Jandan, Nisar Ahmed, Afifi, Ahmed J., Irfan, Muhammad, Alqhtani, Samar, Glowacz, Adam, Alqahtani, Ali, Tadeusiewicz, Ryszard, Kantoch, Eliasz, Zheng, Lihong
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container_title Electronics (Basel)
container_volume 10
creator Soomro, Toufique A.
Ali, Ahmed
Jandan, Nisar Ahmed
Afifi, Ahmed J.
Irfan, Muhammad
Alqhtani, Samar
Glowacz, Adam
Alqahtani, Ali
Tadeusiewicz, Ryszard
Kantoch, Eliasz
Zheng, Lihong
description Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease.
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subjects Algorithms
Blood vessels
Diabetic retinopathy
Eye diseases
Image processing
Image segmentation
Machine learning
Medical imaging
Methods
Modules
Morphology
Noise
Preprocessing
Principal components analysis
Retinal images
Screening
Sensitivity
Tactics
title Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation
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