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

Oscar Ramos-Soto, Erick Rodríguez-Esparza, Sandra E. Balderas-Mata, Diego Oliva, Aboul Ella Hassanien, Ratheesh K. Meleppat, Robert J. Zawadzki

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Article number105949
JournalComputer Methods and Programs in Biomedicine
StatePublished - Apr 2021


  • Homomorphic filtering
  • MCET-HHO algorithm
  • Optimized top-hat
  • Retinal blood vessel segmentation

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics


Dive into the research topics of 'An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering'. Together they form a unique fingerprint.

Cite this