This paper’s main goal is to segment retinal blood vessels for the purpose of diagnosing Diabetic Retinopathy. Currently, Diabetic Retinopathy (DR) is one of the most crucial health issues associated with diabetes. Blindness is also caused due to Diabetic Retinopathy. DR develops at varying rates according to the individual. Because of this, there is a tremendous need for the newest technology and approaches to assess DR effectively. The retinal blood vessels are segmented to detect the DR in its early stages. This proposed algorithm helps in the retinal blood vessel segmentation to detect the DR in its early stages. This algorithm starts with the preprocessing of the input retinal images using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) which enhances the vessels. And the metrics used to analyze the results of preprocessing are MSE, PSNR and SSIM. Later this preprocessed image is applied to the segmentation algorithm i.e., Threshold by ISODATA and at the end retinal vessels are extracted and compared with the results Sobel and Canny segmentation. This method is implemented on a accessible dataset named Digital Retinal Images for Vessel Extraction (DRIVE) and results are measured against Accuracy and the results obtained are satisfactory.