Evaluation of vessel diameters in processed medical retinal images
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Date
2024-12
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Educational Society for Excellence
Abstract
This study focuses on evaluating image processing techniques for measuring retinal vessel diameters, a critical aspect of medical image analysis for diagnosing vascular abnormalities such as diabetic retinopathy. Four algorithms, Canny Edge Detection, Marr-Hildreth Filter, Watershed Segmentation, and Chan-Vese Algorithm, were assessed for their segmentation performance and measurement accuracy. A dataset of 70 retinal images from the DRIVE database, comprising both healthy and diabetic retinopathy cases, was used. Each algorithm was implemented in MATLAB and tailored to address challenges like noise, intensity variations, and weak boundaries. Vessel diameters were calculated using a custom MATLAB algorithm based on the full width at half maximum (FWHM) of intensity profiles, with linear interpolation refining the measurements. This work highlights the potential and limitations of these algorithms in achieving accurate and reliable vessel segmentation for medical imaging applications.
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Keywords
Retinal image processing, Diabetic retinopathy, Edge detection, Chan-Vese algorithm, Image segmentation
Citation
Obreja Cristian-Drago? . Evaluation of vessel diameters in processed medical retinal images. International Archives of Integrated Medicine. 2024 Dec; 11(12): 1-8