Retinal Blood Vessel Segmentation Using a Generalized Gamma Probability Distribution Function (PDF) of Matched Filtered

Abstract

Retinal images contain information about the retina's blood vessel structure to predict retinal diseases such as diabetics, obesity, glaucoma, etc. Segmentation of accurate retinal blood vessels is a challenging task in the low background of retinal images. Therefore, we proposed a Generalized Gamma Distribution probability distribution function (pdf) to extract the accurate vascular structure on the retinal images. The proposed approach is divided into processing steps, the Generalized Gamma distribution kernel, and the postprocessing step. In pre-processing, the conversion of a color retinal image into a grayscale image using PCA followed by the CLAHE method and the Toggle Contrast method enhances the grayscale images of the retina. The proposed matched filter of Generalized Gamma distribution generates the MFR images. The postprocessing step extracts the thick vessels and thin retinal blood vessels using the optimal thresholding technique. The results obtained on DIRVE database average accuracy 95.00% and the STARE database 93.85%, respectively.