electronics Article Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation Toufique A. Soomro 1, * , Ahmed Ali 2 , Nisar Ahmed Jandan 3 , Ahmed J. Afifi 4 , Muhammad Irfan 5 , Samar Alqhtani 6 , Adam Glowacz 7 , Ali Alqahtani 6 , Ryszard Tadeusiewicz 8 , Eliasz Kantoch 8 and Lihong Zheng 9   Citation: Soomro, T.A.; Ali, A.; Jandan, N.A.; Afifi, A.J.; Irfan, M.; Alqhtani, S.; Glowacz, A.; Alqahtani, A.; Tadeusiewicz, R.; Kantoch, E.; Zheng, L. Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation. Electronics 2021, 10, 2297. https://doi.org/10.3390/ electronics10182297 Academic Editor: Bhanu Prakash KN Received: 19 August 2021 Accepted: 14 September 2021 Published: 18 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Electronic Engineering, Science and Technology Larkana Campus, Quaid-e-Awam University of Engineering, Larkana 76221, Pakistan 2 Electrical Engineering Department, Sukkur IBA University, Sukkur 65200, Pakistan; ahmedali.shah@iba-suk.edu.pk 3 Ophthalmology Department, Peoples University of Medical And Health Sciences for Women (PUMHSW), Nawabshah Shaheed Benazirabad, Nawabshah 67459, Pakistan; drnisarjandan@gmail.com 4 Computer Vision & Remote Sensing, Technische Universität, 10623 Berlin, Germany; ajaigi@gmail.com 5 Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia; miditta@nu.edu.sa 6 College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; smalqhtani@nu.edu.sa (S.A.); asalqahtany@nu.edu.sa (A.A.) 7 Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland; adglow@agh.edu.pl 8 Department of Biocybernetics and Biomedical Engineering, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland; rtad@agh.edu.pl (R.T.); kantoch@agh.edu.pl (E.K.) 9 School of Computing and Mathematics, Charles Sturt University, Wagga Wagga 2650, Australia; lzheng@csu.edu.au * Correspondence: etoufique@yahoo.com Abstract: 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. Keywords: retinal fundus image; segmentation; enhancement ; morphological techniques; PCA; vessel binary image Electronics 2021, 10, 2297. https://doi.org/10.3390/electronics10182297 https://www.mdpi.com/journal/electronics