A novel vessel segmentation algorithm in color images of the retina

Abstract

Diabetic retinopathy (DR) occurs in patients who have had diabetes for at least five years. Diseased small blood vessels in the back of the eye cause a leakage of protein and blood in the retina. Diagnosis of diabetic retinopathy at early stage can be done through detection of blood vessels of retina. Blood vessel segmentation is a helpful tool in the treatment of diabetic retinopathy. Many studies have been carried out in the last decade in order to get an accurate blood vessel detection and segmentation in retinal images since vascular anomalies are one of the strongest manifestations of DR. Here, we propose a ruled-based algorithm called Star Networked Pixel Tracking to decide whether a processed pixel is a part of a vessel or not. The complement of the gray scale of the green channel from the original image is used. The blood vessels are enhanced by applying a strong adaptive histogram equalization algorithm locally and globally. Morphological operations are implemented in designing a background image to generate a normalized retinal image. In order to enhance the vessels' contrast, mathematical and morphological operations are applied. Noise artifacts that look like small vessels is filtered by the proposed eight-direction network pixel tracking algorithm. The proposed method is evaluated on 20 images of well known public domain DRIVE database. We achieved an accuracy of 95.83%. This is the highest accuracy among the ruled-based methods reported for the DRIVE database.