An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering

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dc.contributor.authorRamos Soto, Oscar
dc.contributor.authorRodríguez Esparza, Erick
dc.contributor.authorBalderas Mata, Sandra Eloisa
dc.contributor.authorOliva, Diego
dc.contributor.authorHassanien, Aboul Ella
dc.contributor.authorMeleppat, Ratheesh K.
dc.contributor.authorZawadzki, Robert J.
dc.date.accessioned2025-11-03T09:47:28Z
dc.date.available2025-11-03T09:47:28Z
dc.date.issued2021-04
dc.date.updated2025-11-03T09:47:28Z
dc.description.abstractBackground 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.en
dc.identifier.citationRamos-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.2021.105949
dc.identifier.doi10.1016/J.CMPB.2021.105949
dc.identifier.eissn1872-7565
dc.identifier.issn0169-2607
dc.identifier.urihttps://hdl.handle.net/20.500.14454/4214
dc.language.isoeng
dc.publisherElsevier Ireland Ltd
dc.rights© 2021 Elsevier B.V.
dc.subject.otherHomomorphic filtering
dc.subject.otherMCET-HHO algorithm
dc.subject.otherOptimized top-hat
dc.subject.otherRetinal blood vessel segmentation
dc.titleAn efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filteringen
dc.typejournal article
dcterms.accessRightsmetadata only access
oaire.citation.titleComputer Methods and Programs in Biomedicine
oaire.citation.volume201
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