Novel Hybrid Approach for Retinal Blood Vessel Detection: Optimizing a Matched Filter with a Genetic Algorithm Toward IoMT Applications

Accurate retinal vessel segmentation is an important component of automated retinal image analysis, supporting remote assessment and connected healthcare applications. However, detecting small retinal blood vessels while limiting false positives remains challenging. This study proposes a hybrid retinal vessel segmentation method that combines optic disc exclusion with a genetic algorithm-optimized Gaussian matched filter (GMF). The method localizes and masks the optic disc and uses a genetic algorithm to optimize three GMF parameters, with the area under the receiver operating characteristic curve (AUC) as the fitness function. Filtering, thresholding, and morphological operations are then applied to generate the final vessel segmentation. The proposed method was evaluated using two public retinal image databases, DRIVE and STARE. On DRIVE, the method achieved an average AUC of 0.9659 and a mean accuracy (MA) of 0.9444, while on STARE it achieved an average AUC of 0.9625 and an MA of 0.9530. The results demonstrate competitive overall retinal vessel segmentation performance, while the proposed optimization specifically targets the response to small retinal vessels. The method therefore shows potential as an image-analysis component for connected retinal screening systems. However, practical IoMT deployment requires further evaluation of computational efficiency, data protection, and resilience to image manipulation.