An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering
| Autor: | Aboul Ella Hassanien, Oscar Ramos-Soto, Ratheesh Kumar Meleppat, Robert J. Zawadzki, Sandra E. Balderas-Mata, Erick Rodríguez-Esparza, Diego Oliva |
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| Rok vydání: | 2021 |
| Předmět: |
Databases
Factual Fundus Oculi Computer science Health Informatics Fundus (eye) 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine Homomorphic filtering Image Processing Computer-Assisted Median filter Segmentation Sensitivity (control systems) Retinal blood vessels business.industry Matched filter Retinal Vessels Pattern recognition Computer Science Applications Artificial intelligence business Algorithms 030217 neurology & neurosurgery Software Smoothing |
| Zdroj: | Computer Methods and Programs in Biomedicine. 201:105949 |
| ISSN: | 0169-2607 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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