Title: A modified Coye algorithm for retinal vessel segmentation

Authors: Sakambhari Mahapatra; Uma Ranjan Jena; Sonali Dash; S. Agrawal

Addresses: Department of Electronics and Telecommunication Engineering, VSS University of Technology, Burla, India ' Department of Electronics and Telecommunication Engineering, VSS University of Technology, Burla, India ' Department of Electronics and Communication Engineering, Raghu Institute of Technology, Vishakhapatnum, India ' Department of Electronics and Telecommunication Engineering, VSS University of Technology, Burla, India

Abstract: According to a scientific study, eyes are the best predictors of numerous disorders including glaucoma, diabetic retinopathy, hypertension, and stroke. An ophthalmologist can learn about the problems by looking at the segmented retinal blood vessel network. The goal of this study is to offer ophthalmologists with reliable segmented retinal blood vessels to help them pinpoint the issue. This work put forwards an automated method of vessel extraction by incorporating curvelet-based enhancement with the Coye algorithm. Further, the segmentation performance is fine-tuned by embodying a pair of complementary gamma functions (PCGF) for contrast improvement. The suggested approach is evaluated on DRIVE and STARE databases and shows outstanding results as compared to state-of-the-art algorithms.

Keywords: curvelet transform; Coye algorithm; gamma transform; pair of complementary gamma function; PCGF; vessel segmentation.

DOI: 10.1504/IJCVR.2023.127302

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.1, pp.73 - 90

Received: 29 Dec 2020
Accepted: 16 Dec 2021
Published online: 30 Nov 2022 *