Detection of Blood Vessels in Optic Disc with Maximum Principal Curvature and Wolf Thresholding Algorithms for Vessel Segmentation and Prewitt Edge Detection and Circular Hough Transform for Optic Disc Detection

Abstract

The retina is the part of the eye that protects parts of light-sensitive cells. The retina consists of four main parts, namely the blood vessel system, fovea, macula and optic discs. Blood vessels are one of the characteristics that can help in the diagnosis of various retinal diseases. This research discusses the application of algorithms for detection of blood vessels in optic discs. The blood vessels are segmented using the Maximum Principal Curvature algorithm. Before the segmentation process, images are filtered using a Gaussian filters and the optic disc removal is performed. After that the segmentation of vessels uses Wolf thresholding to convert images into binary images. The final step in blood vessels segmentation is removing fine lines that are not vessels using morphological operations. Optic disc detection is done using Prewitt edge detection and circular Hough transform. In optic disc detection, the input image is converted to grayscale and then complemented and improved contrast using contrast-limited adaptive histogram equalization. Then, the opening morphology and median filter were performed. After that, the Prewitt edge and circular Hough transform methods are applied to detect the location of the optic disc. After getting the blood vessel segmentation and knowing the location of optic disc, the last stage is combining the results of blood vessel segmentation with the location of the optic disc. The methods applied are quite efficient in detecting blood vessels at the optic location of the disc.