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Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis
Published on: October 28, 2022
An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic
Oscar Ramos-Soto1, Erick Rodríguez-Esparza2, Sandra E Balderas-Mata1
1División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, C.P. 44430, Guadalajara, Jal., Mexico.
Insights
This study introduces a new method for segmenting retinal blood vessels, improving accuracy for both thin and thick vessels. The approach offers competitive performance without high computational costs, aiding in disease diagnosis.
Area of Science:
- Medical imaging analysis
- Computational ophthalmology
- Image processing
Background:
- Retinal blood vessel segmentation is crucial for diagnosing ophthalmic and cardiovascular diseases.
- Accurate segmentation of both thin and thick vessels presents significant challenges.
Purpose of the Study:
- To propose a novel and robust methodology for automatic retinal blood vessel segmentation.
- To address existing challenges in segmenting vessels of varying sizes.
Main Methods:
- A three-stage process: pre-processing (image smoothing), main processing (segmenting thick and thin vessels using optimized filters and MCET-HHO algorithm), and post-processing (morphological operations).
- Utilized optimized top-hat, homomorphic filtering, median filter, and matched filter for vessel segmentation.
- Employed the MCET-HHO multilevel algorithm for thin vessel segmentation.
Main Results:
- The method achieved high performance metrics on the DRIVE and STARE datasets.
- Average accuracy of 0.9667 (DRIVE) and 0.9580 (STARE).
- Demonstrated superior specificity and accuracy compared to leading unsupervised methods.
Conclusions:
- The proposed technique achieves competitive results against state-of-the-art methods.
- Outperforms unsupervised methods in specificity and accuracy.
- Offers precise thin vessel segmentation without the computational cost of supervised methods.
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.
