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Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images
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A novel framework for retinal vessel segmentation using optimal improved frangi filter and adaptive weighted spatial
Sakambhari Mahapatra1, Sanjay Agrawal1, Pranaba K Mishro1
1Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, India.
Computers in Biology and Medicine
|June 29, 2022
Summary
This study introduces an improved Frangi filter and adaptive fuzzy c-means clustering for enhanced retinal vessel segmentation. The novel approach significantly outperforms existing methods in segmenting retinal vasculature from medical images.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Retinal vasculature analysis is crucial for disease diagnosis.
- Challenges in retinal image segmentation include intensity inhomogeneity and variable vessel thickness.
Purpose of the Study:
- To develop an optimized method for accurate retinal vessel segmentation.
- To enhance the performance of existing retinal image analysis techniques.
Main Methods:
- An improved Frangi-based multi-scale filter optimized with modified enhanced leader particle swarm optimization (MELPSO).
- Image segmentation using a novel adaptive weighted spatial fuzzy c-means (AWSFCM) clustering technique.
- Validation on three publicly available retinal image databases.
Main Results:
- The proposed method demonstrates superior performance compared to state-of-the-art techniques.
- Achieved highly accurate segmentation of retinal vessels.
- Effective enhancement of retinal images for improved analysis.
Conclusions:
- The combined Frangi filter optimization and AWSFCM clustering offer an effective solution for retinal vessel segmentation.
- This approach has the potential to improve automated diagnosis of eye diseases.
- The method provides a robust tool for medical image analysis in ophthalmology.
