Precise retinal vessels localization is an important and challenging task. Segmentation of retinal blood vessels becomes more difficult in abnormal images with the presence of diseases like hypertension, diabetes, stroke and other vascular disorders. In this work, a new fast framework for automatic retinal blood vessels extraction and denoising has been proposed. Green channel due to its prominent vessel structure is used as an input to morphological filters to eradicate low frequency noise or geometrical entities, e.g., macula, optic disk and other abnormalities. The Generalized Linear Model (GLM) regression is used for non-uniform contrast enhancement followed by Frangi filter for vasculature based enhancement. Masking operation has performed to extract Region of Interest (ROI) for application of moment-preserving thresholding to separate vessel and background pixels. Finally, postprocessing steps are applied to eliminate unconnected pixels and to obtain final binary image. This technique has been validated on the DRIVE and the STARE databases and contested with other competing techniques. Experimental results indicate that the proposed vessel extraction framework outperforms many recent existing methods published in literature.