Bonferroni Mean Pre-Aggregation Operator Assisted Dynamic Fuzzy Histogram Equalization for Retinal Vascular Segmentation

Vasculature feature segmentation from the fundus images is critical for the identification of retinal diseases. However, automated vessel segmentation is challenging owing to variability in vessel structure and color gradients, low contrast between vessels, and pathologies. The state-of-the-art approaches address the challenges of emerging hand-crafted filters to apprehend vessel- like patterns. More recently, deep learning-based methods have evolved for the vessel segmentation task, which employs annotations to train the model. These approaches ignore the vessel's geometrical characteristics in the fundus images, leading to inaccuracies. Herein, we propose a novel unsupervised segmentation method based on interrelationship handling, Bonferroni mean pre-aggregation operator, with the aid of dynamic fuzzy histogram equalization, namely BMPDFHESeg. The method extracts the vascular information by fusing color channels by constructing an interrelationship handling pre-aggregation operator. The operator enables finding the direction of increasingness to segment large vessels and vessel feature enhancement through a dynamic fuzzy histogram equalization process using the prior feature intensity information. BMPDFHESeg is assessed qualitatively and quantitatively using the DRIVE, STARE, and HRF datasets, demonstrating enhanced efficacy and computational speed. Further, the results were validated by ophthalmologists for the accuracy of the vessel segmentation and usefulness for the diagnosis of retinal disorders.