TY - JOUR
T1 - An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering
AU - Ramos-Soto, Oscar
AU - Rodríguez-Esparza, Erick
AU - Balderas-Mata, Sandra E.
AU - Oliva, Diego
AU - Hassanien, Aboul Ella
AU - Meleppat, Ratheesh K.
AU - Zawadzki, Robert J.
N1 - Publisher Copyright:
© 2021
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/4
Y1 - 2021/4
N2 - Background 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.
AB - Background 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.
KW - Homomorphic filtering
KW - MCET-HHO algorithm
KW - Optimized top-hat
KW - Retinal blood vessel segmentation
UR - http://www.scopus.com/inward/record.url?scp=85100693673&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100693673&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2021.105949
DO - 10.1016/j.cmpb.2021.105949
M3 - Article
AN - SCOPUS:85100693673
VL - 201
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
SN - 0169-2607
M1 - 105949
ER -