A robust algorithm for segmentation of blood vessels in the presence of lesions in retinal fundus images

Diabetic Retinopathy is a vascular disorder caused by the variations in blood vessels pattern of retina which appears as lesions. Segmentation of blood vessels in the presence of lesions is a challenging task. This paper proposes a robust method for accurate segmentation of blood vessels from retinal images even in the presence of lesions. The proposed method involves three main steps to cater false vessels. First step is to extract region based features from the candidate vascular regions of priori segmented vascular pattern. A discriminant feature vector for each candidate region is formulated. Then feature selection is performed to get the best features. Lastly, blood vessels classification is performed using Support Vector Machine (SVM) classifier to categorize each region as true vessel and false vessel. The proposed approach is tested on the dataset of fundus images from normal and abnormal DR patients and gives good segmentation results even in the presence of lesions.