Retinal blood vessel segmentation using root Guided decision tree assisted enhanced Fuzzy C-mean clustering for disease identification

A number of groundbreaking computer-aided techniques have recently been established for the segmentation of retinal vessels, with clinical applications predominating. Early detection of these abnormalities is critical in preventing individuals from becoming blind. In recent days, automatic vessel segmentation is necessary, but it is a difficult task due to the complex structure and variation in vessel size and shape of retinal vessels. Therefore, in this work, a novel retinal Blood Vessel Segmentation (BVS) has been proposed that utilizes the potential of the Enhanced Fuzzy C-mean Clustering (EFCM) and Root Guided Decision Tree (RGTC). Initially, the input images are pre-processed to enhance the color channel contrast and the Gabor filter is utilized to enhance the small vessels. In the segmentation phase, a 13-dimensional feature vector is constructed, which is then reduced into an optimum feature set by utilizing Principal Component Analysis (PCA). The segmentation phase is utilized to further process the new feature vector. The RGDT is used to undertake reanalysis in non-vessel clusters in order to recover the incorrectly grouped vessel pixels. Finally, the accurate segmented image is obtained through post-processing morphological operations. Simulation has been performed using Python with OpenCV and an accuracy of 97.69% and 97.51% has been achieved using in STARE dataset and DRIVE dataset respectively. Experimental results indicate that the proposed algorithm delivers satisfactory results. As a result, this method is useful as an automated retinal BVS system to assist ophthalmologists.