Gradient features and optimal thresholding for retinal blood vessel segmentation

Retinal blood vessel segmentation is a fundamental step in diagnosis, screening, treatment and evaluation of diabetes retinopathy. Manual segmentation of retinal blood vessels is a long and tedious task and requires trained graders, thus automatic segmentation of retinal blood vessels is a fundamental step in development of computer based diagnostic system for diabetic retinopathy. This paper presents a method for segmentation of retinal blood vessels based on gradient between vessel pixels and background pixels. Due to the intensity variation between retinal blood vessels and background, gradient features of vessel and non vessel pixels can be used for segmentation. Green component of the input retinal image is extracted and first order gradient features are computed using 3 × 3 gradient kernel. The magnitude of the gradient is observed to be maximum at the blood vessels due to intensity variations between vessel and non vessel pixels. Optimal thresholding is then performed on gradient features and retinal blood vessels are segmented. Median filtering is used to reduce salt and pepper noise and length filtering is used to remove isolated pixels. The algorithm is tested on publicly available DRIVE database. The overall accuracy of 92.04 %, sensitivity of 82.44 % and specificity of 95.10 % is observed.