Retinal fundus images provide a large amount of space for blood vessel segmentation. The result of segmentation is not only a vital indicator of diabetic retinopathy (an eye-related disease), but also a good facilitator for ophthalmologists (eye specialists or eye doctors) to carry out essential diagnostic procedures. Diabetic Retinopathy, especially in diabetic patients, is manifested when tiny blood vessels get bleed. In this paper, a novel method called Singular Value Decomposition (SVD) based Maximum Principal Curvatures (SVD-PC) using Level Set Procedure, from retinal images and segmentation of blood vessel vascular structure is proposed. Here, in this proposed method, SVD, which is a mathematical technique related to vector calculus and matrix algebra, is used to efficiently deal with the complexities aroused working with green channel and filters. SVD, in our proposed method, as a pre-processing technique is effective in: (i) extracting colored feature set of blood vessel pixels from input image (ii) effectively converting feature set pixel image into gray. Later, maximum principal curvature values of those converted gray image feature set pixels are calculated in the post-processing phase, followed by ISOData Thresholding to segment the tree-shaped vasculature, which morphological operator is applied to remove the unwanted falsely segmented vessels. The algorithm is implemented in MATLAB and has given segmentation accuracy of 97.8%. This proposed algorithm operated on images of STARE well as DRIVE datasets.