Information of retinal vasculature morphology is being used in grading the severity and progression of diabetic retinopathy. An image analysis system could assist ophthalmologist in making accurate diagnoses in an efficient manner. In this paper, the development of an image processing algorithm for detecting and reconstructing of retinal vasculature is presented. The detection of the vascular structure was achieved by image enhancement using contrast limited adaptive histogram equalization followed by the extraction of the vessels using Bottom-hat morphological transformation. For reconstruction of the complete retinal vasculature,a region growing technique based on first-order Gaussian derivative was developed. The technique incorporates both gradient magnitude change and average intensity as the homogeneity criteria that enabled the process to adapt to intensity changes and intensity spread over the vasculature region. The reconstruction technique reduces the required number of seeds to near optimal for the region growing process. It also overcame poor performance of current seed-based methods especially in low and inconsistent contrast images as normally seen in vasculature regions of fundus images. Simulations of the algorithm on 20 test images from the DRIVE database showed that it outperformed many other published methods and achieved an accuracy (ability to detect both vessel and non-vessel pixels) range of 0.91-0.95,a sensitivity (ability to detect vessel pixels) range of 0.91-0.95 and a specificity (ability to detect non-vessel pixels) range of 0.88-0.94.