Adaptive Preprocessing Optimization for Retinal Vascular Network Segmentation
DOI:
https://doi.org/10.46842/ipn.cien.v29n2a05Keywords:
image segmentation, fundus eye image, blood vessels, homomorphic filter, PSOAbstract
The segmentation of the vascular network in fundus images is a key step in the diagnosis and monitoring of ophthalmological and systemic pathologies, such as diabetic retinopathy (RD). However, the reliability of automated methods is challenged by inherent image issues, mainly non-uniform illumination, low contrast, and noise. This work proposes a methodology for the automated preprocessing and segmentation of fundus images, addressing these problems through an adaptation and regularization strategy. The method begins with the extraction of the green channel (G) to maximize contrast. Then, a Gaussian filter is applied, with its smoothing parameter (σ) optimized for each image by balancing noise reduction and detail preservation using the Variance of Laplacian (VoL) and High-Frequency Energy Residual (HFER). Next, a homomorphic filter is applied to correct illumination, with its parameters dynamically adjusted using a Particle Swarm Optimization (PSO) algorithm, employing Shannon entropy as the objective function. Finally, the Frangi filter is used to enhance tubular structures, followed by controlled binarization. The method was validated on the public DRIVE and STARE databases, where the homomorphic optimization stage showed a consistent increase in image entropy (e.g., from 5.7 to 6.2 bits/pixel), improving contrast. The proposed adaptive preprocessing strategy helps improve the reliability of vascular segmentation and, consequently, the diagnosis of related pathologies.
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