Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Electronics

Samar Alqhtani¹  Ali Alqahtani¹  Ahmed J. Afifi²  Adam Glowacz³  Eliasz Kantoch⁴  Ryszard Tadeusiewicz⁴  Toufique A. Soomro⁵  Muhammad Irfan⁶  Ahmed Ali⁷  Nisar Ahmed Jandan⁸  Lihong Zheng⁹ 

[1] College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia;Computer Vision & Remote Sensing, Technische Universität, 10623 Berlin, Germany;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;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;Department of Electronic Engineering, Science and Technology Larkana Campus, Quaid-e-Awam University of Engineering, Larkana 76221, Pakistan;Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia;Electrical Engineering Department, Sukkur IBA University, Sukkur 65200, Pakistan;Ophthalmology Department, Peoples University of Medical And Health Sciences for Women (PUMHSW), Nawabshah Shaheed Benazirabad, Nawabshah 67459, Pakistan;School of Computing and Mathematics, Charles Sturt University, Wagga Wagga 2650, Australia;

关键词: retinal fundus image;    segmentation;    enhancement;    morphological techniques;    PCA;    vessel binary image;   

DOI  :  10.3390/electronics10182297

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown