Ramos Soto, OscarRodríguez Esparza, ErickBalderas Mata, Sandra EloisaOliva, DiegoHassanien, Aboul EllaMeleppat, Ratheesh K.Zawadzki, Robert J.2025-11-032025-11-032021-04Ramos-Soto, O., Rodríguez-Esparza, E., Balderas-Mata, S. E., Oliva, D., Hassanien, A. E., Meleppat, R. K., & Zawadzki, R. J. (2021). An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering. Computer Methods and Programs in Biomedicine, 201. https://doi.org/10.1016/J.CMPB.2021.1059490169-260710.1016/J.CMPB.2021.105949https://hdl.handle.net/20.500.14454/4214Background and objective: Automatic segmentation of retinal blood vessels makes a major contribution in CADx of various ophthalmic and cardiovascular diseases. A procedure to segment thin and thick retinal vessels is essential for medical analysis and diagnosis of related diseases. In this article, a novel methodology for robust vessel segmentation is proposed, handling the existing challenges presented in the literature. Methods: The proposed methodology consists of three stages, pre-processing, main processing, and post-processing. The first stage consists of applying filters for image smoothing. The main processing stage is divided into two configurations, the first to segment thick vessels through the new optimized top-hat, homomorphic filtering, and median filter. Then, the second configuration is used to segment thin vessels using the proposed optimized top-hat, homomorphic filtering, matched filter, and segmentation using the MCET-HHO multilevel algorithm. Finally, morphological image operations are carried out in the post-processing stage. Results: The proposed approach was assessed by using two publicly available databases (DRIVE and STARE) through three performance metrics: specificity, sensitivity, and accuracy. Analyzing the obtained results, an average of 0.9860, 0.7578 and 0.9667 were respectively achieved for DRIVE dataset and 0.9836, 0.7474 and 0.9580 for STARE dataset. Conclusions: The numerical results obtained by the proposed technique, achieve competitive average values with the up-to-date techniques. The proposed approach outperform all leading unsupervised methods discussed in terms of specificity and accuracy. In addition, it outperforms most of the state-of-the-art supervised methods without the computational cost associated with these algorithms. Detailed visual analysis has shown that a more precise segmentation of thin vessels was possible with the proposed approach when compared with other procedures.eng© 2021 Elsevier B.V.Homomorphic filteringMCET-HHO algorithmOptimized top-hatRetinal blood vessel segmentationjournal article2025-11-031872-7565