Retinal blood vessel segmentation using density-based fuzzy C-means clustering and vessel neighborhood connected component

Retinal blood vessel segmentation is a crucial process in medical image analysis. However, it is often challenging due to the variation in color, shape, intensity, size, and contrast of blood vessels. Most of the relevant studies concentrate on algorithms based on supervised learning and few on deep learning. However, due to the several challenges in retinal image acquisition, these algorithms cannot deliver the highest possible level of accuracy. Therefore, this paper implements retinal blood vessel segmentation and classification (RBVSC) methods using density-based fuzzy C-means clustering and vessel neighborhood-connected components, hereafter denoted as DBFCM-VNCC. Initially, the given retinal images are preprocessed using the contrast enhancement method that involves Histogram Equalization with Variable Enhancement Degree (HEVED), selecting the appropriate color channel, optic disc elimination, and using a Gaussian filter, which removes the noise or artifacts from the retinal images. Then, a fully fuzzy C-means clustering is used for coarse segmentation of vessel lesions, which can detect the affected blood vessel features quite efficiently. Finally, structure-based algorithms based on vessel neighborhood-connected components based on mathematical dilation operators and local thicknesses are used to obtain accurate skeletonization and segmentation of the retinal vessels. The algorithm was assessed using three open-access retinal image databases: DRIVE, CHASE_DB1, and HRF, where it achieved mean sensitivity, specificity, accuracy, area overlap measure, and error rate scores of 98.16%, 98.74%, 97.68%, and 4.54%; 98.25%, 98.81%, 97.68%, 97.86%, and 2.14%; 98.22%, 98.78%, 97.56%, 97.40%, and 2.60% for segmenting the retinal vessel. This demonstrates the effectiveness of the proposed DBFCM-VNCC techniques.