Retinal vessel segmentation takes a significant part in an automated diabetic retinopathy screening task. However, this can be a challenging job because of the low contrast retinal images and the presences of retinal pathologies. Hence, in this paper, we propose a novel matched filter based on the modified Chebyshev type I function for retinal blood vessels candidates detection. The proposed method is combined with the pre-processing and the post-processing phases to have a complete retinal vessel segmentation scheme. The retinal images from the DRIVE and STARE databases, which are equipped with the ground truths are used to evaluate our proposed method in the segmentation scheme. Using our method, the achieved average levels of sensitivity, specificity, and accuracy are 0.756, 0.973, and 0.954, for the DRIVE database, and 0.731, 0.972, and 0.953, for the STARE database, being better than other presented methods. The high results indicate that our method is reliable to be used in an automated detection tool for diabetic retinopathy.